clib.conversion._to_numpy: Add tests for pandas.Series with pyarrow date32/date64 types

[seisman]

Description of proposed changes

Add tests for panda.Series with pyarrow date32/date64 dtypes.

In PR #2845, we added the support of date32[day][pyarrow] and date64[ms][pyarrow] and mapped them to np.datetime64. This PR improves it and maps them to "datetime64[D]" and datetim64[ms] respectively.

After approval, I plan to cherry-pick changes in e13b5d3 into a separate PR so that we can have a changelog entry.

[seisman]

Ping @weiji14 for final reviews on the date32/date64 behavior changes.

[weiji14]

Maybe update the test here too to check explicitly for "datetime64[D]"/"datetim64[ms]" instead of just np.datetime64? Or does it not matter I guess?

pygmt/pygmt/tests/test_clib_vectors_to_arrays.py

Lines 89 to 100 in 9a12ae9

	vectors= [
	pd.Series(
	data=[datetime.date(2020, 1, 1), datetime.date(2021, 12, 31)],
	dtype="date32[day][pyarrow]",
	),
	pd.Series(
	data=[datetime.date(2022, 1, 1), datetime.date(2023, 12, 31)],
	dtype="date64[ms][pyarrow]",
	),
	]
	arrays=vectors_to_arrays(vectors)
	assertall(i.dtype.type==np.datetime64foriinarrays)

[seisman]

Actually I feel the test test_vectors_to_arrays_pyarrow_datetime can be removed, since it's already covered by the newly added tests in this PR.

[weiji14]

Actually I feel the test test_vectors_to_arrays_pyarrow_datetime can be removed, since it's already covered by the newly added tests in this PR.

On second thought, maybe keep the test. we need to test that vectors_to_arrays can handle mixed dtypes (and that test checks both date32/date64) from a pandas.DataFrame or nested list, whereas _to_numpy is for single dtypes on a pandas.Series or single list.