BUG: Mean of zeros turn out to be non-zero
- [ ] 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.

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

```python
'''Two examples of a time-weighted rolling average, both of which should result in a final value of 0.'''
import pandas as pd


def succeeding_test():
    df = pd.DataFrame(
        {"A": [300239975158033.0, -0.0, -0.0]},
        index=[
            pd.Timestamp("19700101 09:00:00"),
            pd.Timestamp("19700101 09:00:03"),
            pd.Timestamp("19700101 09:00:06"),
        ],
    )
    v = df.resample("1s").ffill().rolling("3s", closed="left", min_periods=3).mean().values[-1]
    assert v == 0


def failing_test():
    df = pd.DataFrame(
        {"A": [3002399751580331.0, -0.0, -0.0]},  # First value is a single digit longer.
        index=[
            pd.Timestamp("19700101 09:00:00"),
            pd.Timestamp("19700101 09:00:03"),
            pd.Timestamp("19700101 09:00:06"),
        ],
    )
    v = df.resample("1s").ffill().rolling("3s", closed="left", min_periods=3).mean().values[-1]
    assert v == 0  # v turns out to be == -0.3333333
```

#### Problem description

In both these cases, the final rolling mean value is the mean of three zeros, but in the `failing_test`, this does not result in a zero value. I am expecting the `failing_test` to have the same behaviour as the `succeeding_test`. The difference lies in the magnitude of the first value in the df, but that shouldn't affect the last mean, as it falls outside the rolling window for that value.

#### Expected Output

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

<details>

INSTALLED VERSIONS
------------------
commit           : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python           : 3.6.9.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.4.0-42-generic
Version          : #46-Ubuntu SMP Fri Jul 10 00:24:02 UTC 2020
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.1.1
numpy            : 1.16.4
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.2.2
setuptools       : 46.1.1
Cython           : None
pytest           : 4.6.11
hypothesis       : 4.57.1
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : 2.8.4 (dt dec pq3 ext lo64)
jinja2           : 2.11.2
IPython          : 7.16.1
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.1
numexpr          : 2.7.1
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 1.0.1
pytables         : None
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : 1.3.13
tables           : 3.6.1
tabulate         : None
xarray           : 0.16.0
xlrd             : 1.2.0
xlwt             : None
numba            : 0.47.0

</details>
