DataTable init does not not replace NaNs with pd.NA with float data type

- We want DataTable to use 1 representation of NaN (pd.NA). This is a forwarding looking feature of pandas.
> The goal of pd.NA is provide a “missing” indicator that can be used consistently across data types (instead of np.nan, None or pd.NaT depending on the data type).

- In our init of DataTable, we have a `replace_none`, which defaults to True. However, this is not working for some data types inputted into the DataTable
```python
import numpy as np
import pandas as pd
import woodwork as ww
d = {'col1': [1, 2, np.nan], 'col2': [3, 4, None],
'col3': pd.Series([1, 2, np.nan], dtype='Int64'),
'col4': pd.Series([1, 2, None], dtype='string')}
df = pd.DataFrame(data=d)

df.dtypes

dt = ww.DataTable(df, name="retail", replace_none=True, copy_dataframe=True)
dt.dataframe
```

- The expected behavior is that all NaN-like values in the DataFrame would be pd.NA