REGR: perf regression in Series.combine_first
- [x] closes #55845
- [x] [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
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [x] Added an entry in the latest `doc/source/whatsnew/v2.2.1.rst` file if fixing a bug or adding a new feature.

```
import pandas as pd
import numpy as np

N = 1_000_000


# different indexes
s1 = pd.Series(np.random.randn(N))
s1 = s1[::2].sample(frac=1.0)
s2 = pd.Series(np.random.randn(N))
%timeit s1.combine_first(s2)

# 523 ms ± 7.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)   <- main
# 227 ms ± 4.33 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)  <- PR


# same index
s1 = pd.Series(np.random.randn(N))
s1[::2] = np.nan
s2 = pd.Series(np.random.randn(N))
%timeit s1.combine_first(s2)

# 282 ms ± 5.64 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)   <- main
# 7 ms ± 64.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)  <- PR
```
Thanks @lukemanley 