fix(presentation): Correct type hint to fix Pydantic serialization warning (#317)

## Summary

This PR fixes a PydanticSerializationUnexpectedValue warning that is
raised when generating a benchmark report (e.g., in HTML format).

The warning Expected float - serialized value may not be as expected
[input_value='p50', input_type=str] occurs because the percentile_rows
method in the TabularDistributionSummary class has an incorrect return
type hint.

The method generates a list of dictionaries, where each dictionary
contains a string value for the 'percentile' key (e.g., 'p50') and a
float value for the 'value' key. The type hint list[dict[str, float]]
incorrectly stated that all dictionary values would be floats.

## Details


## Test Plan

guidellm benchmark run --target <any-target>
--output-path=benchmarks.html

## Related Issues

#314

- Resolves #

This fix corrects the type hint for the percentile_rows method to
list[dict[str, Union[str, float]]], accurately describing the data being
returned. This resolves the serialization warning from Pydantic without
changing any runtime logic.

  Before:
   1 def percentile_rows(self) -> list[dict[str, float]]:
  After:
   1 def percentile_rows(self) -> list[dict[str, Union[str, float]]]:

- [x] "I certify that all code in this PR is my own, except as noted
below."

## Use of AI

- [ ] Includes AI-assisted code completion
- [ ] Includes code generated by an AI application
- [ ] Includes AI-generated tests (NOTE: AI written tests should have a
docstring that includes `## WRITTEN BY AI ##`)

Signed-off-by: xinjun.jiang <[email protected]>
Co-authored-by: Samuel Monson <[email protected]>

Author: git-jxj

src/guidellm/presentation/data_models.py

@@ -190,7 +190,7 @@ class TabularDistributionSummary(DistributionSummary):
     """
 
     @computed_field
-    def percentile_rows(self) -> list[dict[str, float]]:
+    def percentile_rows(self) -> list[dict[str, Union[str, float]]]:
         rows = [
             {"percentile": name, "value": value}
             for name, value in self.percentiles.model_dump().items()