PERF: faster constructors from ea scalars
- [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 an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.

Perf improvement when constructing a DataFrame/Series from a scalar EA value.

Examples:

```
import pandas as pd

N = 1_000_000


%timeit pd.DataFrame({"A": pd.NA, "B": 1.0}, index=range(N), dtype=pd.Float64Dtype())
2.18 s ± 214 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)       <- main
22.4 ms ± 2.81 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)   <- PR


%timeit pd.Series(pd.NA, index=range(N), dtype=pd.Float64Dtype())
1.62 s ± 50.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)      <- main
7.33 ms ± 1.05 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)  <- PR


%timeit pd.Series(1, index=range(N), dtype='Int64')
242 ms ± 21.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)      <- main
11.6 ms ± 231 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)   <- PR
```
very nice @lukemanley keep em coming!