CLN/PERF: no need for kahan for int group_cumsum
Surprised at lack of impact here - doesn't noticeably affect benchmarks.

Targeting the cython algo specifically shows an improvement (but smaller than I'd expect given the removed operations):
```
import numpy as np
import pandas._libs.groupby as libgroupby

N = 4_000_000
vals = np.random.randint(0, 10, (N, 5), dtype=np.int64)
result = np.empty_like(vals)

%timeit libgroupby.group_cumsum(result, vals, np.ones(N, dtype="int"), 1, False)
# 28.9 ms ± 805 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)  # this pr
# 37.3 ms ± 206 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)   # master
```