PERF: ArrowExtensionArray.to_numpy(dtype=object)
- [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).
- [x] 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/v2.0.0.rst` file if fixing a bug or adding a new feature.

Perf improvement for `ArrowExtensionArray.to_numpy(dtype=object)`. 

(`dtype=object` is not uncommon, see discussion in #22791) 

Some examples where it has an impact:

```
import pandas as pd
import numpy as np

data = np.random.randn(1_000_000, 10)
df = pd.DataFrame(data, dtype="float64[pyarrow]")

%timeit df.sum(axis=1)

# 4.04 s ± 53.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)  <- main
# 796 ms ± 22.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)  <- PR

%timeit df.clip(0.0, 0.1)

# 4.82 s ± 159 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)   <- main
# 1.51 s ± 10.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)  <- PR
```
dumb question: why isn't `self._data.to_numpy()` good enough here? > dumb question: why isn't `self._data.to_numpy()` good enough here?

I believe there is a desire to maintain the raw dtype (even if wrapped in an object array) and use `pd.NA` by default. 

For instance, this converts int to float when nulls are present:

```
In [1]: import pandas as pd

In [2]: arr = pd.array([1, None], dtype="int64[pyarrow]")

In [3]: arr._data.to_numpy()
Out[3]: array([ 1., nan])
```

I'm not sure its required, but the current implementation aligns mostly with `BaseMaskedArray`:

```
In [4]: arr = pd.array([1, None], dtype="Int64")

In [5]: arr.to_numpy()
Out[5]: array([1, <NA>], dtype=object)

In [6]: arr = pd.array([1, None], dtype="int64[pyarrow]")

In [7]: arr.to_numpy()
Out[7]: array([1, <NA>], dtype=object)
```

 Thanks @lukemanley  Not totally sure, but looks like this is failing the ci on main. Any idea why? Ah darn. Should we revert this in the meantime? Probably a good idea, if we don't know how to fix Yeah locally reverting this change fixes the test failure