Lose the index before 'na_value' assignment in 'to_numpy'
- [X] closes #45774 
- [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 an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.

This Pull Request addresses the issue where casting a multi-indexed Series `to_numpy` fails if an `na_value` is supplied. See the issue #45774 for more information.

The choice to use `asanyarray` over other solutions to lose the index is one based on performance and simplicity. The following simple benchmark was used to make this determination:

```python
import time
import pandas as pd
import numpy as np

t0 = time.time()

series = pd.Series(range(100000000))

#res = series.isna() # Orignal buggy: Elapsed time:  0.19312691688537598 sec
#res = tuple(series.isna()) # Elapsed time:  5.303609132766724 sec
#res = series.isna().reset_index(drop=True) # Elapsed time:  0.2203369140625 sec
#res = series.isna().to_numpy()  # Elapsed time:  0.19327998161315918 sec
#res = np.array(series.isna())  # Elapsed time:  0.25583529472351074 sec
res = np.asanyarray(series.isna())  # Elapsed time:  0.19619202613830566 sec

t1 = time.time()

#print(res)
print("Elapsed time: ", t1 - t0)
```

This demonstrates that there is no significant overhead to using the `to_numpy` operation.

thanks @DamianBarabonkovQC 