BUG: .fillna({}) doesn't work
### 

- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.

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


### Reproducible Example

```python
import pandas as pd

df = pd.DataFrame([1,2,np.nan, pd.NA], columns=['a'])

# case 1
df.fillna({})

# case 2
df.fillna({np.nan: {}, pd.NA: {}})
```


### Issue Description

.fillna() doesn't work as expected for case then `value = {}`

as written in ```pandas.DataFrame.fillna```:

> DataFrame.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None)
> 
> Fill NA/NaN values using the specified method.
> 
> Parameters
> 
> value scalar, dict, Series, or DataFrame
> 
> Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the dict/Series/DataFrame will not be filled. This value cannot be a list.
> 


### Expected Behavior

all values (`np.nan` and `pd.NA`) should be changed to `{}`

### Installed Versions

<details>

INSTALLED VERSIONS
------------------
commit           : 73c68257545b5f8530b7044f56647bd2db92e2ba
python           : 3.9.6.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.10.47-linuxkit
Version          : #1 SMP Sat Jul 3 21:51:47 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           : 1.3.3
numpy            : 1.20.3
pytz             : 2021.1
dateutil         : 2.8.2
pip              : 21.2.4
setuptools       : 58.0.4
Cython           : 0.29.24
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 3.0.1
IPython          : 7.27.0
pandas_datareader: None
bs4              : 4.10.0
bottleneck       : 1.3.2
fsspec           : 2021.08.1
fastparquet      : None
gcsfs            : None
matplotlib       : 3.4.3
numexpr          : 2.7.3
odfpy            : None
openpyxl         : 3.0.9
pandas_gbq       : None
pyarrow          : 5.0.0
pyxlsb           : None
s3fs             : None
scipy            : 1.7.1
sqlalchemy       : 1.4.23
tables           : 3.6.1
tabulate         : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
numba            : 0.54.0
</details>
