Groupby multiple columns causes agg to have precision loss on int64
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd

df=pd.DataFrame({
    "KEY1": ["KEY1"],
    "KEY2": ["KEY2"],
    "INT64": [1583715738627261039]
})

# Grouping by one column produces correct output
print(df.groupby(["KEY1"]).agg(lambda x: x))

# Grouping by more than one column produces incorrect output
print(df.groupby(["KEY1", "KEY2"]).agg(lambda x: x))
```
#### Problem description

Precision loss on the int64 column when grouping by multiple columns.

Similar behaviour can be seen when doing
```
>>> df.groupby(["KEY1"]).sum()
                    INT64
KEY1                     
KEY1  1583715738627260928
```
but that seems like a different issue with cythonized group sum not supporting int64? According to: https://github.com/pandas-dev/pandas/issues/15027#issuecomment-269902341

#### Expected Output
```
      KEY2                INT64
KEY1                           
KEY1  KEY2  1583715738627261039
                         INT64
KEY1 KEY2                     
KEY1 KEY2  1583715738627261039
```
#### Actual Output
```
      KEY2                INT64
KEY1                           
KEY1  KEY2  1583715738627261039
                         INT64
KEY1 KEY2                     
KEY1 KEY2  1583715738627260928
```


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

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.5.final.0
python-bits      : 64
OS               : Linux
OS-release       : 3.16.0-77-generic
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_AU.UTF-8
LOCALE           : en_AU.UTF-8

pandas           : 1.0.3
numpy            : 1.18.2
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 46.1.3
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : None
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : None

</details>
