Supported data types by the private `_to_numpy` function

Here is a central issue tracking the data types the private `_to_numpy` function supports. We will add tests for these data types in a series of PRs. Please note that it doesn't mean that PyGMT support these data types.

### Numeric Dtypes

- [x] Python built-in types #3583
- int
- float
- complex
- [x] NumPy array: #3583
- int8, int16, int32, int64, longlong
- uint8, uint16, uint32, uint64, ulonglong
- float16, float32, float64, longdouble
- complex64, complex128, clongdouble
- [x] pandas.Series with NumPy dtypes #3583
- int8, int16, int32, int64, longlong
- uint8, uint16, uint32, uint64, ulonglong
- float16, float32, float64, longdouble
- complex64, complex128, clongdouble
- [x] pandas.Series with pandas/PyArrow dtypes #3584
- Int8, Int16, Int32, Int64
- UInt8, UInt16, UInt32, UInt64
- Float32, Float64
- int8[pyarrow], int16[pyarrow], int32[pyarrow], int64[pyarrow]
- uint8[pyarrow], uint16[pyarrow], uint32[pyarrow], uint64[pyarrow]
- float16[pyarrow], float32[pyarrow], float64[pyarrow]
- [x] pyarrow.array with PyArrow dtypes #3599
- int8, int16, int32, int64
- uint8, uint16, uint32, uint64
- float16, float32, float64

### String Dtypes

- [X] Python's built-in list of strings #3601
- [x] NumPy array with string dtype #3601
- None
- np.str_
- "U10"
- [x] pandas.Series with string dtype #3607
- None
- np.str_
- "U10"
- string[python]
- string[pyarrow]
- string[pyarrow_numpy]
- [x] pyarrow.array with string dtype #3608
- None
- string
- utf8
- large_string
- large_utf8
- string_view

### Datetime dtypes

- [ ] Python sequence of datetime-like objects: #3758
- [x] [ISO 8601](https://en.wikipedia.org/wiki/ISO_8601) string format
- [x] datetime.date
- [x] datetime.datetime
- [ ] datetime.time
- [x] np.datetime64
- [x] pandas.Timestamp
- [ ] pyarrow.TimestampScalar
- [ ] NumPy `datetime64` #3687
- [x] date unit: Y, M, W, D
- [x] time unit: h, m, s, ms, us, ns
- [ ] time unit: ps, fs, as
- [ ] pandas.Series
- [x] `np.datetime64` #3670
- [x] `pandas.DatetimeTZDtype` #3670
- [x] `pa.timestamp` #3670
- [x] `pa.date32`/`date32[D][pyarrow]` #3610
- [x] `pa.date64`/`date64[ms][pyarrow]` #3610
- [ ] `pa.time32`
- [ ] `pa.time64`
- [ ] pyarrow.array
- [x] date32 #3609
- [x] date64 #3609
- [x] timestamp #3621
- [ ] time32
- [ ] time64

### Timedelta dtypes

### Bool