BUG: int64 and object/float64 series do not fill NA values identically
- [x] I have checked that this issue has not already been reported.

- [x] I have confirmed this bug exists on the latest version of pandas.

- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.

---

#### Code Sample, a copy-pastable example

```python
s1 = pd.Series([pd.NA, -1], dtype='Int64')
s2 = pd.Series([pd.NA, -1])  # the dtype is now object

```

#### Problem description

`s1.fillna(np.nan)` and `s2.fillna(np.nan)` return different series. Specifically, the first one keeps the `pd.NA` cell untouched and does not replace it with `np.nan`. Replacing with any other value that `np.nan` does not trigger this bug.

#### Expected Output

```
> s2.fillna(np.nan)
0    NaN
1   -1.0
dtype: float64
```

#### Output of ``pd.show_versions()``

<details>


INSTALLED VERSIONS
------------------
commit           : f00ed8f47020034e752baf0250483053340971b0
python           : 3.8.5.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.4.0-210-generic
Version          : #242-Ubuntu SMP Fri Apr 16 09:57:56 UTC 2021
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : en_US.UTF-8
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.3.0
numpy            : 1.19.1
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.2.2
setuptools       : 50.3.0
Cython           : 0.29.17
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.18.1
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.2
numexpr          : 2.7.1
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : 1.5.2
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
