check ExtensionType in is_datetime64_any_dtype for array-likes
- [x] closes #57055
- [x] 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
- [x] 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.
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
Failures in CI look unrelated to this PR ([logs](https://github.com/pandas-dev/pandas/actions/runs/7647380880/job/20838187996#step:8:730))
> FAILED pandas/tests/arrays/categorical/test_sorting.py::TestCategoricalSort::test_numpy_argsort - Failed: DID NOT RAISE <class 'ValueError'>
FAILED pandas/tests/extension/test_categorical.py::TestCategorical::test_argsort - AssertionError: Series are different Thanks @mroeschke. The test is failing ([log](https://app.circleci.com/pipelines/github/pandas-dev/pandas/42146/workflows/f2ed35b6-c1e4-44ef-8b7b-4c6a535db568/jobs/142394)) for arrow date types

> FAILED pandas/tests/extension/test_arrow.py::test_is_datetime64_any_dtype[date32[day]] - AssertionError
FAILED pandas/tests/extension/test_arrow.py::test_is_datetime64_any_dtype[date64[ms]] - AssertionError

since for those types `pa.types.is_timestamp` returns `False`. However the current implementation of `is_datetime64_any_dtype` returns `True` when provided as a dtype, e.g. `pd.api.types.is_datetime64_any_dtype(serie_with_pyarrow_date32.dtype)`. What should I do here? Is it expected for that to return `True` or should I adjust the above condition to also check if `pa.types.is_date` returns `False`?

**Edit**
I checked that both of those types translate to datetime64 in numpy:
```python
import pyarrow as pa

type(pa.date32().to_pandas_dtype())
# numpy.dtypes.DateTime64DType 
type(pa.date64().to_pandas_dtype())
# numpy.dtypes.DateTime64DType
```
so I've included those in the test for `True`  I think the date types should return False. In pandas they don't always behave like date _times_ Agree, for example trying to extract the second raises an error. I'll make the changes. Thanks @jmoralez 