BUG: Regression from 1.2.5 to 1.3.x:  groupby using sum on DataFrame containing lists fails
- [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.

---

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

Version 1.3.2:

```python
>>> df=pd.DataFrame([["M", [1]], ["M", [2]], ["W", [10]], ["W", [20]]], columns=["MW", "v"])
>>> df.groupby("MW").sum()
Empty DataFrame
Columns: []
Index: [M, W]
>>> df.groupby("MW").v.sum()
MW
M      [1, 2]
W    [10, 20]
Name: v, dtype: object
```

Version 1.2.5:
```python
>>> df=pd.DataFrame([["M", [1]], ["M", [2]], ["W", [10]], ["W", [20]]], columns=["MW", "v"])
>>> df.groupby("MW").sum()
           v
MW
M     [1, 2]
W   [10, 20]
>>> df.groupby("MW").v.sum()
MW
M      [1, 2]
W    [10, 20]
Name: v, dtype: object
```
#### Problem description

With 1.2.5 and earlier, if the elements of one of the columns are objects that are lists, then the `groupby` operation with `sum` creates the aggregation of the lists.  With 1.3.2, it doesn't.  

#### Expected Output

Same as 1.2.5

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

<details>

INSTALLED VERSIONS
------------------
commit           : 3af1a4fa27a7d48d8e6aea66bf2da6fdf2e1550e
python           : 3.8.8.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
Version          : 10.0.19041
machine          : AMD64
processor        : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : English_United States.1252

pandas           : 1.4.0.dev0+475.g3af1a4fa27
numpy            : 1.21.1
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 21.1.3
setuptools       : 49.6.0.post20210108
Cython           : 0.29.23
pytest           : 6.2.4
hypothesis       : 6.14.1
sphinx           : 3.5.4
blosc            : None
feather          : None
xlsxwriter       : 1.4.3
lxml.etree       : 4.6.3
html5lib         : 1.1
pymysql          : None
psycopg2         : None
jinja2           : 2.11.3
IPython          : 7.25.0
pandas_datareader: None
bs4              : 4.9.3
bottleneck       : 1.3.2
fsspec           : 2021.05.0
fastparquet      : 0.6.3
gcsfs            : 2021.05.0
matplotlib       : 3.3.3
numexpr          : 2.7.3
odfpy            : None
openpyxl         : 3.0.7
pandas_gbq       : None
pyarrow          : 2.0.0
pyxlsb           : None
s3fs             : 0.4.2
scipy            : 1.7.0
sqlalchemy       : 1.4.20
tables           : 3.6.1
tabulate         : 0.8.9
xarray           : 0.18.2
xlrd             : 2.0.1
xlwt             : 1.3.0
numba            : 0.52.0

</details>
