BUG: groupby / resample / aggregate produces 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 of pandas.

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

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**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 = """        date  revenue name
0 2020-02-24        9  n_1
1 2020-05-12        8  n_2
2 2020-03-28        9  n_2
3 2020-01-14        2  n_0"""

df = pd.read_csv(StringIO(data), sep = "\s+", engine = "python")
df.date = pd.to_datetime(df.date)     

gb = df.groupby("name").resample("M", on="date")

res1 = gb.sum()[['revenue']]

# ==>
#                  revenue
# name date               
# n_0  2020-01-31        2
# n_1  2020-02-29        9
# n_2  2020-03-31        9
#      2020-04-30        0
#      2020-05-31        8

res2 = gb.aggregate({'revenue':'sum'})

# ==>
#                  revenue
# name date               
# n_0  2020-05-31        2
# n_1  2020-01-31        9
# n_2  2020-02-29        8
#      2020-03-31        9

```

#### Problem description

As the code sample above shows, there are two issues: 

* `agg(sum)` produces different results from just `sum`. 
* The results of `agg(sum)` are plain wrong. The name "n_2" has no data in Feb, and yet - the aggregation shows that it does. 

#### Expected Output

The correct output is the one produced by `sum`. 

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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.5.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 19.5.0
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.0.5
numpy            : 1.17.4
pytz             : 2019.3
dateutil         : 2.8.0
pip              : 19.3.1
setuptools       : 41.6.0
Cython           : None
pytest           : 5.4.3
hypothesis       : 5.18.0
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : 1.2.9
lxml.etree       : 4.5.0
html5lib         : 1.0.1
pymysql          : None
psycopg2         : 2.8.4 (dt dec pq3 ext lo64)
jinja2           : 2.10.3
IPython          : 7.10.1
pandas_datareader: 0.8.1
bs4              : 4.9.0
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.5.0
matplotlib       : 3.2.1
numexpr          : None
odfpy            : None
openpyxl         : 3.0.3
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : 5.4.3
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : 1.3.16
tables           : None
tabulate         : None
xarray           : 0.15.1
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : 1.2.9
numba            : None

</details>
