BUG: get_indexer_non_unique() does not always handle NaNs correctly
### 

- [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
import numpy as np

idx_no_nan = pd.Index([1, 2], dtype=np.float64)
idx_nan = pd.Index([1, 2, np.nan])

print("dtype=np.float64")
target = pd.Index([1, np.nan], dtype=np.float64)
print(idx_no_nan.get_indexer(target))
print(idx_no_nan.get_indexer_non_unique(target))
print(idx_nan.get_indexer(target))
print(idx_nan.get_indexer_non_unique(target))

print("dtype='object'")
target = pd.Index([1, np.nan], dtype='object')
print(idx_no_nan.get_indexer(target))
print(idx_no_nan.get_indexer_non_unique(target))
print(idx_nan.get_indexer(target))
print(idx_nan.get_indexer_non_unique(target))
```


### Issue Description

Annotated script output
```
dtype=np.float64
[ 0 -1]
(array([ 0, -1]), array([1]))  # ← expected
[0 2]
(array([ 0, -1]), array([1])) #  ← would expect (array([0, 2]), array([], dtype=int64)
dtype='object'
[ 0 -1]
(array([0, 0, 1]), array([], dtype=int64)) # ← would expect (array([0, -1]), array([1]))
[0 2]
(array([0, 2]), array([], dtype=int64) # ← expected
```


### Expected Behavior

As the indexes and the targets are unique I would expect the output of `get_indexer()` and `get_indexer_non_unique()` to be equivalent.
```
dtype=np.float64
[ 0 -1]
(array([ 0, -1]), array([1]))
[0 2]
(array([0, 2]), array([], dtype=int64)
dtype='object'
[ 0 -1]
(array([0, -1]), array([1]))
[0 2]
(array([0, 2]), array([], dtype=int64)
```

### Installed Versions

<details>
INSTALLED VERSIONS
------------------
commit           : a07561e5a33b78cb54d163e81dd7a4444ca41554
python           : 3.8.12.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.4.0-90-lowlatency
Version          : #101-Ubuntu SMP PREEMPT Fri Oct 15 20:57:56 UTC 2021
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : de_DE.UTF-8
LOCALE           : de_DE.UTF-8

pandas           : 1.4.0.dev0+1117.ga07561e5a3
numpy            : 1.21.4
pytz             : 2021.3
dateutil         : 2.8.2
pip              : 21.3.1
setuptools       : 58.5.3
Cython           : 0.29.24
pytest           : 6.2.5
hypothesis       : 6.24.2
sphinx           : 4.2.0
blosc            : None
feather          : None
xlsxwriter       : 3.0.2
lxml.etree       : 4.6.4
html5lib         : 1.1
pymysql          : None
psycopg2         : None
jinja2           : 3.0.3
IPython          : 7.23.1
pandas_datareader: None
bs4              : 4.10.0
bottleneck       : 1.3.2
fsspec           : 2021.11.0
fastparquet      : 0.7.1
gcsfs            : 2021.11.0
matplotlib       : 3.4.3
numexpr          : 2.7.3
odfpy            : None
openpyxl         : 3.0.9
pandas_gbq       : None
pyarrow          : 6.0.0
pyxlsb           : None
s3fs             : 2021.11.0
scipy            : 1.7.2
sqlalchemy       : 1.4.26
tables           : 3.6.1
tabulate         : 0.8.9
xarray           : 0.18.2
xlrd             : 2.0.1
xlwt             : 1.3.0
numba            : 0.53.1
</details>
