BUG: rolling with time window does not always pick edge correctly
- [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.

- [ ] (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
data = pd.Series(range(1, 4), index=pd.date_range(start="2020-01-01", end="2020-01-03", freq="1d")).iloc[::-1]
data.rolling("1d", closed="left").sum()
```

#### Problem description

I'm expecting the result to be 
```
2020-01-03   NaN
2020-01-02   3.0
2020-01-01   2.0
Freq: -1D, dtype: float64
```
but instead I get
```
2020-01-03   NaN
2020-01-02   NaN
2020-01-01   NaN
Freq: -1D, dtype: float64
```

This problem doesn't occur if `window` is `int` instead of `offset`, or if index is in ascending order. 
For example:
`data.rolling(1, closed="left").sum()` yields
```
2020-01-03    NaN
2020-01-02    3.0
2020-01-01    2.0
Freq: -1D, dtype: float64
```
and 
```
data = pd.Series(range(1, 4), index=pd.date_range(start="2020-01-01", end="2020-01-03", freq="1d"))
data.rolling("1d", closed="left").sum()
``` 
yields
```
2020-01-01    NaN
2020-01-02    1.0
2020-01-03    2.0
Freq: D, dtype: float64
```

#### Expected Output
```
2020-01-03   NaN
2020-01-02   3.0
2020-01-01   2.0
Freq: -1D, dtype: float64
```

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

<details>

INSTALLED VERSIONS
------------------
commit           : 9d598a5e1eee26df95b3910e3f2934890d062caa
python           : 3.7.9.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
Version          : 10.0.19041
machine          : AMD64
processor        : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : None.None

pandas           : 1.2.1
numpy            : 1.19.2
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 20.3.3
setuptools       : 52.0.0.post20210125
Cython           : 0.29.21
pytest           : 6.2.2
hypothesis       : None
sphinx           : 3.4.3
blosc            : None
feather          : None
xlsxwriter       : 1.3.7
lxml.etree       : 4.6.2
html5lib         : 1.1
pymysql          : 1.0.2
psycopg2         : None
jinja2           : 2.11.3
IPython          : 7.20.0
pandas_datareader: None
bs4              : 4.9.3
bottleneck       : 1.3.2
fsspec           : 0.8.3
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.2
numexpr          : 2.7.2
odfpy            : None
openpyxl         : 3.0.6
pandas_gbq       : None
pyarrow          : 0.15.1
pyxlsb           : None
s3fs             : 0.4.2
scipy            : 1.6.0
sqlalchemy       : 1.3.23
tables           : 3.6.1
tabulate         : 0.8.7
xarray           : 0.16.2
xlrd             : 2.0.1
xlwt             : 1.3.0
numba            : 0.51.2

</details>
