perf: improve performance of array_repeat function (#20049)

## Which issue does this PR close?

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- Closes #.

## Rationale for this change

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The current `array_repeat` implementation built intermediate arrays row
by row using `MutableArrayData + concat`. This caused unnecessary
allocations and per-row overhead.

This PR replaces it with a vectorized implementation based on `offsets +
take`, which constructs the output in a single pass and reduces
intermediate memory usage.

## What changes are included in this PR?

Reimplemented `general_repeat` and `general_list_repeat` using `take`,
eliminating per-row materialization.

### Benchmarks
```
group                                               after                                   before
-----                                               -----                                   ------
array_repeat_boolean/repeat_50_count/100            1.00     18.1±0.59µs        ? ?/sec     5.92    106.8±3.29µs        ? ?/sec
array_repeat_boolean/repeat_50_count/1000           1.00    100.0±8.00µs        ? ?/sec     9.34   934.2±20.43µs        ? ?/sec
array_repeat_boolean/repeat_50_count/10000          1.00   882.5±18.07µs        ? ?/sec     10.45     9.2±0.16ms        ? ?/sec
array_repeat_boolean/repeat_5_count/100             1.00     10.3±0.58µs        ? ?/sec     6.82     70.6±2.74µs        ? ?/sec
array_repeat_boolean/repeat_5_count/1000            1.00     20.5±1.05µs        ? ?/sec     28.07  574.6±20.67µs        ? ?/sec
array_repeat_boolean/repeat_5_count/10000           1.00    119.1±6.41µs        ? ?/sec     47.08     5.6±0.12ms        ? ?/sec
array_repeat_float64/repeat_50_count/100            1.00     15.0±0.34µs        ? ?/sec     6.47     96.9±4.63µs        ? ?/sec
array_repeat_float64/repeat_50_count/1000           1.00     68.6±2.91µs        ? ?/sec     12.23  839.5±29.00µs        ? ?/sec
array_repeat_float64/repeat_50_count/10000          1.00   700.7±29.21µs        ? ?/sec     12.21     8.6±0.21ms        ? ?/sec
array_repeat_float64/repeat_5_count/100             1.00      9.7±0.27µs        ? ?/sec     7.12     69.2±1.75µs        ? ?/sec
array_repeat_float64/repeat_5_count/1000            1.00     18.2±0.81µs        ? ?/sec     30.75  558.7±19.02µs        ? ?/sec
array_repeat_float64/repeat_5_count/10000           1.00     98.8±8.05µs        ? ?/sec     55.35     5.5±0.23ms        ? ?/sec
array_repeat_int64/repeat_50_count/100              1.00     15.5±0.70µs        ? ?/sec     6.46     99.8±2.94µs        ? ?/sec
array_repeat_int64/repeat_50_count/1000             1.00     69.3±3.29µs        ? ?/sec     12.42  860.6±14.99µs        ? ?/sec
array_repeat_int64/repeat_50_count/10000            1.00   672.0±20.81µs        ? ?/sec     12.54     8.4±0.14ms        ? ?/sec
array_repeat_int64/repeat_5_count/100               1.00      9.6±0.51µs        ? ?/sec     6.03     57.7±1.94µs        ? ?/sec
array_repeat_int64/repeat_5_count/1000              1.00     17.9±0.73µs        ? ?/sec     32.14  574.2±22.44µs        ? ?/sec
array_repeat_int64/repeat_5_count/10000             1.00     99.2±6.90µs        ? ?/sec     55.65     5.5±0.20ms        ? ?/sec
array_repeat_nested_int64/repeat_50_count/100       1.00     53.6±2.21µs        ? ?/sec     2.67    143.0±5.55µs        ? ?/sec
array_repeat_nested_int64/repeat_50_count/1000      1.00   474.7±30.74µs        ? ?/sec     2.95  1398.8±40.50µs        ? ?/sec
array_repeat_nested_int64/repeat_50_count/10000     1.00     14.3±0.41ms        ? ?/sec     1.39     19.9±2.98ms        ? ?/sec
array_repeat_nested_int64/repeat_5_count/100        1.00     14.2±0.49µs        ? ?/sec     6.62     93.8±3.66µs        ? ?/sec
array_repeat_nested_int64/repeat_5_count/1000       1.00     57.1±1.81µs        ? ?/sec     14.09  803.8±18.81µs        ? ?/sec
array_repeat_nested_int64/repeat_5_count/10000      1.00   509.5±27.43µs        ? ?/sec     18.27     9.3±0.36ms        ? ?/sec
array_repeat_nested_string/repeat_50_count/100      1.00    138.6±5.27µs        ? ?/sec    1.60   221.4±10.37µs        ? ?/sec
array_repeat_nested_string/repeat_50_count/1000     1.00  1300.5±49.34µs        ? ?/sec    1.70      2.2±0.05ms        ? ?/sec
array_repeat_nested_string/repeat_50_count/10000    1.00     28.6±1.21ms        ? ?/sec    1.06     30.3±1.91ms        ? ?/sec
array_repeat_nested_string/repeat_5_count/100       1.00     24.2±1.61µs        ? ?/sec    4.37    106.1±3.95µs        ? ?/sec
array_repeat_nested_string/repeat_5_count/1000      1.00    144.5±4.22µs        ? ?/sec    7.04  1016.5±39.20µs        ? ?/sec
array_repeat_nested_string/repeat_5_count/10000     1.00  1471.1±60.22µs        ? ?/sec    8.11     11.9±0.60ms        ? ?/sec
array_repeat_string/repeat_50_count/100             1.00     30.9±2.19µs        ? ?/sec     4.52    139.8±5.38µs        ? ?/sec
array_repeat_string/repeat_50_count/1000            1.00    231.5±4.96µs        ? ?/sec     5.25  1214.5±23.72µs        ? ?/sec
array_repeat_string/repeat_50_count/10000           1.00      4.0±0.12ms        ? ?/sec     3.18     12.9±0.49ms        ? ?/sec
array_repeat_string/repeat_5_count/100              1.00     11.5±0.35µs        ? ?/sec     6.47     74.5±1.81µs        ? ?/sec
array_repeat_string/repeat_5_count/1000             1.00     35.6±1.21µs        ? ?/sec     16.66  593.5±14.59µs        ? ?/sec
array_repeat_string/repeat_5_count/10000            1.00   284.3±26.10µs        ? ?/sec     21.21     6.0±0.14ms        ? ?/sec
```

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## Are these changes tested?
Yes. Existing SLT tests pass without modification.

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## Are there any user-facing changes?
No.
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Author: lyne7-sc

datafusion/functions-nested/Cargo.toml

@@ -88,3 +88,7 @@ name = "map"
 [[bench]]
 harness = false
 name = "array_remove"
+
+[[bench]]
+harness = false
+name = "array_repeat"