BUG: Inconsistency between behavior of cumsum when applied directly, and when applied within groupby.
### 

- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.

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


### Reproducible Example

```python
import pandas as pd
df = pd.DataFrame({"A": [2, 1, 2, 2],
                       "B": [3, 3, 4, 4],                       
                       "E": [['10'], ['20'], ['30'], ['40']]})
df["A"].cumsum()
df["E"].cumsum()

df.groupby("B")["A"].cumsum()
df.groupby("B")["E"].cumsum()
```


### Issue Description

Inconsistency between behavior of cumsum() when applied directly, and when applied within groupby().
When used directly on a column of a dataframe that contains lists, cumsum() progressively concatenates the lists.
When used as part of groupby() on the same dataframe cumsum() throws "NotImplementedError: function is not implemented for this dtype: [how->cumsum,dtype->object]"

### Expected Behavior

I'd expect cumsum() to progressively concatenate the lists of all rows within each group defined by groupby(), in a way similar to what it does with numeric values.

### Installed Versions

<details>

INSTALLED VERSIONS
------------------
commit           : 73c68257545b5f8530b7044f56647bd2db92e2ba
python           : 3.8.2.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.15.0-147-generic
Version          : #151-Ubuntu SMP Fri Jun 18 19:21:19 UTC 2021
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.3.3
numpy            : 1.19.2
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.1.1
setuptools       : 46.1.3
Cython           : 0.29.21
pytest           : 6.2.2
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : 1.2.9
lxml.etree       : 4.5.2
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.16.1
pandas_datareader: None
bs4              : 4.9.1
bottleneck       : 1.3.2
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.2.2
numexpr          : 2.7.1
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 0.16.0
pyxlsb           : None
s3fs             : None
scipy            : 1.5.0
sqlalchemy       : 1.3.18
tables           : 3.6.1
tabulate         : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
numba            : 0.53.1

</details>
