FIX: Solving Int64 precision loss when read_csv(StringIO(data), dtype…
…={"a": "Int64"}, engine=pyarrow)

[] closes [BUG: read_csv losing precision when reading Int64[pyarrow] data with N/A values #56135](https://github.com/pandas-dev/pandas/issues/56135) and [BUG: read_csv loses precision when engine='pyarrow' and dtype Int64 #56136](https://github.com/pandas-dev/pandas/issues/56136)
     [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
     All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
     Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.

Enable the integer_object_nulls option by setting it to True in the following line of code in the "arrow_parser_wrapper.py" file:
table.to_pandas(types_mapper=self.kwds["dtype"].get, integer_object_nulls=True)
This adjustment is crucial when using the "pyarrow" engine as a parameter for the 'read_csv' function. It ensures that columns containing integers (Int64) with null elements within a dataframe maintain their precision. Without this setting, numpy may treat columns with null elements by converting the entire column data to Float, leading to precision loss. Enabling 'integer_object_nulls' prevents this issue and preserves the precision of integer data.

Thanks for the PR, but appears this is already being worked on in https://github.com/pandas-dev/pandas/pull/56251 so closing in favor of that PR