confusion_matrix: Remove nullable type handling when possible

[tamargrey]

There are two ways we could avoid having to explicitly set the numpy dtypes of nullable data after a Series.to_numpy call.

• Pandas stops converting nullable dtypes to object in Series.to_numpy call: API: should to_numpy() by default return the corresponding type, and raise otherwise? pandas-dev/pandas#48891 - then we could just call to_numpy and not have to worry about setting the correct dtype
• Sklearn supports the nullable dtypes in unique_labels (Support nullable pandas dtypes in unique_labels scikit-learn/scikit-learn#25634) and sklearn_confusion_matrix (Support nullable pandas dtypes in confusion_matrix scikit-learn/scikit-learn#25635) - currently neither do, which is why we handle the dtype changes prior to making those calls. Once support is added, we can stop converting the data to numpy arrays before passing in y true and predicted

If either of the above are completed and released, we can upgrade to that version and make any needed changes to remove nullable type handling from #4019.

[tamargrey]

Both the related sklearn issues have been fixed, so whenever that work is released and we upgrade to that sklearn version, we should be able to complete this issue.