REF: use WrappedCythonOp for GroupBy.std, sem
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.

Slight slowdown (but there _should_ be a decreased memory footprint) for `std`, pretty nice speedup for `sem`:

```
import pandas as pd
import numpy as np

arr = np.random.randn(10**5, 3)
df = pd.DataFrame(arr)
gpr = np.arange(2).repeat(len(arr) // 2)
gb = df.groupby(gpr)

%timeit gb.std()
790 µs ± 55.9 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)  # <- main
803 µs ± 34.4 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)  # <- PR

%timeit gb.sem()
2.73 ms ± 150 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)    # <- main
796 µs ± 25.4 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)  # <- PR
```
Thanks @jbrockmendel 