Calling `sum` with `min_count` on `SeriesGroupBy` with dtype `Int64` gives large negative value rather than pd.NA
```python

test_df = pd.DataFrame({'foo' : ['a'], 'bar': [1]})
test_df['bar'] = test_df['bar'].astype('Int64')
test_df.groupby('foo')['bar'].sum(min_count=2)

# output:
foo
a    -9223372036854775808
Name: bar, dtype: Int64

```
#### Problem description

Per the documentation, `sum` should return `NA` if there are fewer than `min_count` values. This works fine on the dataframe itself:

```python
test_df['bar'].sum(min_count=2)

# output
nan
```

but gives what looks like an overflow error when called after `groupby`.

I ran into this with real data when calling with `min_count=1` on a dataframe where some of the values were missing, but I thought the minimal example above was clearer.


#### Expected Output

foo
a    NaN
Name: bar, dtype: Int64

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

<details>

[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.3.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.15.0-74-generic
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_GB.UTF-8
LOCALE           : en_GB.UTF-8

pandas           : 1.0.3
numpy            : 1.18.1
pytz             : 2019.1
dateutil         : 2.8.0
pip              : 19.1.1
setuptools       : 41.0.1
Cython           : None
pytest           : 5.3.4
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.3.3
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.10.1
IPython          : 7.5.0
pandas_datareader: None
bs4              : 4.8.1
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.3.3
matplotlib       : 3.1.2
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : 5.3.4
pyxlsb           : None
s3fs             : None
scipy            : 1.3.0
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None
numba            : None
</details>
