BUG: Rolling count aggregation produces unexpected results
- [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.

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

---

```python
>>> pd.__version__
'1.1.0.dev0+1690.g70d7c04ff'
>>> pd.Series([1,1,1,None]).rolling(2, min_periods=2, center=True).count()
0    NaN
1    2.0
2    2.0
3    1.0
dtype: float64
```

#### Problem description

Apologies if this is expected behavior and not a bug. I noticed that prior to version 1.0, `rolling.count` ignored the `min_periods` parameter, as discussed in https://github.com/pandas-dev/pandas/pull/30923. However I'm having trouble understanding this output. Shouldn't the last element of the output be `NaN`, given that the final window includes the last and second to last elements, of which only one is valid?

#### Expected Output

```
0    NaN
1    2.0
2    2.0
3    NaN
dtype: float64
```

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

<details>

INSTALLED VERSIONS
------------------
commit           : 70d7c04ff585de361622e4fe1788480a7a4526b5
python           : 3.8.3.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.15.0-76-generic
Version          : #86-Ubuntu SMP Fri Jan 17 17:24:28 UTC 2020
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.1.0.dev0+1690.g70d7c04ff
numpy            : 1.18.1
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 46.4.0.post20200518
Cython           : 0.29.17
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : None
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None
</details>
