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

- [] closes #56135 and #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.
Doing what you are describing here has huge performance implications, I don't want to do this here Thanks for the pull request, but it appears to have gone stale. If interested in continuing, please merge in the main branch, address any review comments and/or failing tests, and we can reopen.