BUG: rolling with uneven nanosecond window have wrong results
### 

- [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

index = pd.date_range("2020", periods=4, freq="1ns")
df = pd.Series([1, 1, 1, 1], index=index, dtype=float)
result = df.rolling('3ns', closed='neither', center=True).sum()

print(result)
# 2020-01-01 00:00:00.000000000    1.0
# 2020-01-01 00:00:00.000000001    1.0
# 2020-01-01 00:00:00.000000002    1.0
# 2020-01-01 00:00:00.000000003    1.0
# Freq: N, dtype: float64
```


### Issue Description

Rolling with smallest possible resolution (currently nanoseconds) **centered** windows and uneven nanoseconds produce wrong results. 

Eg. rolling with a centered `window='3ns'`, regardless of `closed`, should result in a forward facing and a backward facing window of `1.5ns`, which is effectively a fwd/bwd facing window of `1ns` with `closed='both'`. 

btw. thats exactly the code change in the followup PR will introduce ;)       

### Expected Behavior


```py
>>> result
2020-01-01 00:00:00.000000000    2.0
2020-01-01 00:00:00.000000001    3.0
2020-01-01 00:00:00.000000002    3.0
2020-01-01 00:00:00.000000003    2.0
Freq: N, dtype: float64
```

### Installed Versions

<details>

INSTALLED VERSIONS
------------------
commit           : 075ec87643130074c9389a17a9d964cd33f779dc
python           : 3.8.12.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.4.0-88-generic
Version          : #99-Ubuntu SMP Thu Sep 23 17:29:00 UTC 2021
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8
pandas           : 1.4.0.dev0+869.g075ec87643
numpy            : 1.21.2
pytz             : 2021.1
dateutil         : 2.8.2
pip              : 21.2.4
setuptools       : 58.2.0
Cython           : 0.29.24
pytest           : 6.2.5
hypothesis       : 6.23.1
sphinx           : 4.2.0
blosc            : None
feather          : None
xlsxwriter       : 3.0.1
lxml.etree       : 4.6.3
html5lib         : 1.1
pymysql          : None
psycopg2         : None
jinja2           : 3.0.1
IPython          : 7.28.0
pandas_datareader: None
bs4              : 4.10.0
bottleneck       : 1.3.2
fsspec           : 2021.10.0
fastparquet      : 0.7.1
gcsfs            : 2021.10.0
matplotlib       : 3.4.3
numexpr          : 2.7.3
odfpy            : None
openpyxl         : 3.0.9
pandas_gbq       : None
pyarrow          : 5.0.0
pyxlsb           : None
s3fs             : 2021.10.0
scipy            : 1.7.1
sqlalchemy       : 1.4.25
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>
