BUG: Series.replace with categorical dtype and null values coerces to object

[rhshadrach]

ser = pd.Series(pd.Categorical([1], categories=[1, 2, 3]))
print(ser)
# 0    1
# dtype: category
# Categories (3, int64): [1, 2, 3]

ser = ser.replace(1, None)
print(ser)
# 0    NaN
# dtype: category
# Categories (2, object): [2, 3]

One gets the same behavior if None is replaced by pd.NA, and similar behavior with np.nan but this is coerced to float. Since the categories can be integers and the Series still hold NA values, it seems to me this shouldn't coerce to object.

ser = pd.Series(pd.Categorical([None], categories=[1, 2, 3]))
print(ser)
# 0    NaN
# dtype: category
# Categories (3, int64): [1, 2, 3]

Probably unrelated: #46884

[lukemanley]

#50857 is an existing PR that fixes this. I added a test for this in that PR.