BUG: idxmin and idxmax fail for groupby of decimal columns
- [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.

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

---

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

```python
# Your code here
import pandas as pd
from decimal import Decimal
# from pandas.tests.extension.decimal import DecimalArray

df = pd.DataFrame({
    'idx': [0, 1],
    'x': [Decimal('8.68'), Decimal('42.23')],
    'y': [Decimal('7.11'), Decimal('79.61')],
    # 'x': DecimalArray([Decimal('8.68'), Decimal('42.23')]),
    # 'y': DecimalArray([Decimal('7.11'), Decimal('79.61')]),
})

print(df.groupby("idx", sort=True).agg('idxmin'))
# Empty DataFrame
# Columns: []
# Index: [0, 1]
```

#### Problem description

When a column contains `Decimal` objects, the `idxmin` and `idxmax` aggregations return empty output.

#### Expected Output
The expected output is
```
     x  y
idx
0    0  0
1    1  1
```

If I uncomment the commented lines in the above example (i.e. place the `Decimal` objects inside a `DecimalArray`), it works, but AFAICT that isn't really part of the intended public pandas API. Moreover, the issue is not exclusively due to singleton groups: I can also reproduce it using the following DataFrame:

```
df = pd.DataFrame({
    'idx': [0, 1, 0, 1],
    'x': [Decimal('8.68'), Decimal('42.23'), Decimal('8.69'), Decimal('42.24')],
    'y': [Decimal('7.11'), Decimal('79.61'), Decimal('7.12'), Decimal('79.62')],
})
```

I don't think this is related to #39098 because this only occurs for `idxmin` or `idxmax`, not aggregations like `sum`. That is the only obviously related issue I could find.

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

<details>

I've tested on two separate systems.

**Docker Ubuntu container (running on a host Ubuntu machine)**

INSTALLED VERSIONS
------------------
commit           : f2c8480af2f25efdbd803218b9d87980f416563e
python           : 3.7.10.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.15.0-76-generic
Version          : #86-Ubuntu SMP Fri Jan 17 17:24:28 UTC 2020
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : en_US.UTF-8

pandas           : 1.2.3
numpy            : 1.20.1
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 21.0.1
setuptools       : 52.0.0.post20210125
Cython           : 0.29.22
pytest           : 6.2.2
hypothesis       : 6.3.4
sphinx           : 3.5.1
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.3
IPython          : 7.21.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : 0.8.7
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.4
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 1.0.1
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : 0.53.0

**conda Python on a Mac**

INSTALLED VERSIONS
------------------
commit           : f2c8480af2f25efdbd803218b9d87980f416563e
python           : 3.9.2.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 19.6.0
Version          : Darwin Kernel Version 19.6.0: Tue Jan 12 22:13:05 PST 2021; root:xnu-6153.141.16~1/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.2.3
numpy            : 1.20.1
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 21.0.1
setuptools       : 49.6.0.post20210108
Cython           : 0.29.22
pytest           : 6.2.2
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.3
IPython          : 7.21.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None

</details>
