REF: use more explicit to_numpy(object) instead of astype(object) in EA implementation
Somewhat related to https://github.com/pandas-dev/pandas/issues/24877 / https://github.com/pandas-dev/pandas/pull/43699, but regardless of whether we want to stop `astype` returning numpy arrays or not, I think when we know we want to get an object-dtype numpy array as result, using `to_numpy(object)` is more explicit about this when reading the code.
Can't speak to the general case, but DTA is more performant in astype

```
In [3]: dti = pd.date_range('2016-01-01', periods=10**5, freq='s', tz="US/Pacific")

In [4]: dta = dti._data

In [5]: %timeit dta.astype(object)
50.8 ms ± 2.13 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [6]: %timeit dta.to_numpy(object)
178 ms ± 6.26 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
``` Good catch. That seems to be because in datetimelike `__array__` we explicitly go through a list, which shouldn't be needed:

https://github.com/pandas-dev/pandas/blob/fefccfe10a34d244ec1df0bba5792fbc894c4402/pandas/core/arrays/datetimelike.py#L313-L316

We can probably call astype there (or do the same than astype does). Astype special cases object dtype for datetime64 to directly call `ints_to_pydatetime`:

https://github.com/pandas-dev/pandas/blob/fefccfe10a34d244ec1df0bba5792fbc894c4402/pandas/core/arrays/datetimelike.py#L410-L422

(which is what `list(self)` will also call, but then iteratively) xref #44484 for dispatch logic Yes, although the behaviour differences I note in that issue are for other dtypes, not object dtype (for object, it's only a matter of performance, as far as I see) This pull request is stale because it has been open for thirty days with no activity. Please [update](https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#updating-your-pull-request) and respond to this comment if you're still interested in working on this. 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.