BUG: `df.combine_first` duplicates rows for nan index values
- [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.
        (the issue sounds familiar, but I couldn't find it anywhere...)
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.

---

**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.

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

```python
>>> df1 = pd.DataFrame({'a': [1, 2, 3], 'b': [np.nan, 5, 6], 'x': [9, 10, 11]})
>>> df1_ = df1.set_index(['a', 'b'])
>>> df1_
        x
a b      
1 NaN   9
2 5.0  10
3 6.0  11


>>> df2 = pd.DataFrame({'a': [1, 2, 4], 'b': [np.nan, 5, 7], 'y': [12, 13, 14]})
>>> df2_ = df2.set_index(['a', 'b'])
>>> df2_
        y
a b      
1 NaN  12
2 5.0  13
4 7.0  14


>>> df1_.combine_first(df2_)
          x     y
a b              
1 NaN   9.0  12.0
  NaN   9.0  12.0   <--- duplicated row
2 5.0  10.0  13.0
3 6.0  11.0   NaN
4 7.0   NaN  14.0

```

#### Problem description

When combining two DataFrames with overlapping indices, the indices, which contain `nan` values but are otherwise identical, are duplicated.

The problem doesn't occur when the `nan` values are substituted with say `-1`.

#### Expected Output

```python
>>> df1_.combine_first(df2_)
          x     y
a b              
1 NaN   9.0  12.0
2 5.0  10.0  13.0
3 6.0  11.0   NaN
4 7.0   NaN  14.0
```

It is expected that `combine_first` detects that the index `[1, NaN]` exists in both dataframes and thus does not duplicate this row in the resulting dataframe.

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

<details>

INSTALLED VERSIONS
------------------
commit           : 7d32926db8f7541c356066dcadabf854487738de
python           : 3.8.7.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 19.6.0
Version          : Darwin Kernel Version 19.6.0: Tue Jan 12 22:13:05 PST 2021; root:xnu-6153.141.16~1/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_AU.UTF-8
LOCALE           : en_AU.UTF-8

pandas           : 1.2.2
numpy            : 1.19.4
pytz             : 2020.4
dateutil         : 2.8.1
pip              : 20.2.3
setuptools       : 46.4.0
Cython           : 0.29.21
pytest           : 5.1.1
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : 0.9.6
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : 2.8.3 (dt dec pq3 ext lo64)
jinja2           : 2.11.2
IPython          : 7.16.1
pandas_datareader: None
bs4              : None
bottleneck       : 1.3.1
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.0
numexpr          : 2.7.1
odfpy            : None
openpyxl         : 3.0.5
pandas_gbq       : None
pyarrow          : 1.0.1
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : 1.3.12
tables           : 3.6.1
tabulate         : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
numba            : None

</details>
