BUG: any and all are much slower than min and max for GroupBy.transform
- [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.

---

**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.

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

```python
import numpy as np
import pandas as pd
pd.__version__
N=10**6
z=pd.DataFrame({'a': np.random.randint(1, 10, N), 'b': np.random.randint(1, 100000, N)})
z['c'] = (z.b % 2).astype(bool)
%timeit z.groupby('a').c.transform(max)
%timeit z.groupby('a').c.transform(min)
%timeit z.groupby('a').c.transform(any)
%timeit z.groupby('a').c.transform(all)
```

#### Problem description

Logically any  or all on bools should be faster or as fast as max or min which give exactly the same results.
```
In [7]: %timeit z.groupby('a').c.transform(max)                                                             
11.8 ms ± 86.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [8]: %timeit z.groupby('a').c.transform(min)                                                             
11.8 ms ± 78.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [9]: %timeit z.groupby('a').c.transform(any)                                                             
99.7 ms ± 383 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [10]: %timeit z.groupby('a').c.transform(all)                                                            
99.8 ms ± 665 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
```


#### Expected Output

Times for any and all are lower or same as for max and min

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

<details>

INSTALLED VERSIONS
------------------
commit           : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python           : 3.8.6.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.8.18-200.fc32.x86_64
Version          : #1 SMP Mon Nov 2 19:49:11 UTC 2020
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.1.4
numpy            : 1.19.4
pytz             : 2020.4
dateutil         : 2.8.1
pip              : 19.3.1
setuptools       : 41.6.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.4.1
html5lib         : 1.0.1
pymysql          : 0.9.3
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.12.0
pandas_datareader: None
bs4              : 4.9.3
bottleneck       : 1.2.1
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.2.2
numexpr          : 2.7.1
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 2.0.0
pytables         : None
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : None
tables           : 3.5.2
tabulate         : 0.8.7
xarray           : None
xlrd             : 1.2.0
xlwt             : 1.1.2
numba            : None


</details>
