Multicolumn GroupBy appears to convert unit64s to floats
#### Code Sample, a copy-pastable example if possible

```python
pd.DataFrame({'first': [1], 'second': [1], 'value': [16148277970000000000]}).groupby(['first', 'second'])['value'].max()

```
#### Problem description

When that code snippet runs, the result is not `16148277970000000000` as you would expect, but `16148277969999998976`. Note that `int(float(16148277970000000000)) == 16148277969999998976`.

Additional notes:

1. The problem only appears to happen if there are multiple groupby keys. For example just doing `groupby(['first'])` returns the expected result. So does removing the `groupby` statement entirely.
2. The problem is not specific to `max`. I get the same problem for `min`, `first`, `last`, `median`, `mean`, but not `head`, `tail`, or `apply`.
3. The problem also happens if you do `.transform(max)`.
4. The smallest number I've found so far that has a problem is 2**63 + 1


#### Expected Output
16148277970000000000


#### Output of ``pd.show_versions()``
<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.9.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.14.137+
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 0.25.3
numpy            : 1.17.5
pytz             : 2018.9
dateutil         : 2.6.1
pip              : 19.3.1
setuptools       : 42.0.2
Cython           : 0.29.14
pytest           : 3.6.4
hypothesis       : None
sphinx           : 1.8.5
blosc            : None
feather          : 0.4.0
xlsxwriter       : None
lxml.etree       : 4.2.6
html5lib         : 1.0.1
pymysql          : None
psycopg2         : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2           : 2.10.3
IPython          : 5.5.0
pandas_datareader: 0.7.4
bs4              : 4.6.3
bottleneck       : 1.3.1
fastparquet      : None
gcsfs            : 0.6.0
lxml.etree       : 4.2.6
matplotlib       : 3.1.2
numexpr          : 2.7.1
odfpy            : None
openpyxl         : 2.5.9
pandas_gbq       : 0.11.0
pyarrow          : 0.14.1
pytables         : None
s3fs             : 0.4.0
scipy            : 1.4.1
sqlalchemy       : 1.3.12
tables           : 3.4.4
xarray           : 0.14.1
xlrd             : 1.1.0
xlwt             : 1.3.0
xlsxwriter       : None
</details>
