[SPARK-54787][PS] Use list comprehension in pandas _bool_column_labels

### What changes were proposed in this pull request?

Use list comprehension in the pandas.DataFrame method _bool_column_labels. This will modestly improve memory and performance, but also reduces code to a single line.

### Why are the changes needed?

For mantainability and performance

### Does this PR introduce _any_ user-facing change?

No

### How was this patch tested?

CI

### Was this patch authored or co-authored using generative AI tooling?

No

Closes #53550 from devin-petersohn/devin/pandas_maintain_01.

Authored-by: Devin Petersohn <[email protected]>
Signed-off-by: Hyukjin Kwon <[email protected]>

Author: devin-petersohn

python/pyspark/pandas/frame.py

@@ -11268,15 +11268,9 @@ def _bool_column_labels(self, column_labels: List[Label]) -> List[Label]:
         """
         Filter column labels of boolean columns (without None).
         """
-        bool_column_labels = []
-        for label in column_labels:
-            psser = self._psser_for(label)
-            if is_bool_dtype(psser):
-                # Rely on dtype rather than spark type because
-                # columns that consist of bools and Nones should be excluded
-                # if bool_only is True
-                bool_column_labels.append(label)
-        return bool_column_labels
+        # Rely on dtype rather than spark type because columns that consist of bools and
+        # Nones should be excluded if bool_only is True
+        return [label for label in column_labels if is_bool_dtype(self._psser_for(label))]
 
     def _result_aggregated(
         self, column_labels: List[Label], scols: Sequence[PySparkColumn]