PERF/REGR: astype changing order of some 2d data
- [x] closes #42396
- [x] closes #38592
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry

This just lets `numpy` stick with the default of `order="C"` for both `ravel` and `reshape` so no reordering occurs. 

Benchmarks:

```
       before           after         ratio
     [87d78559]       [0c9d5496]
     <master>         <regr_astype>
-      9.34±0.7ms       4.95±0.9ms     0.53  stat_ops.FrameOps.time_op('kurt', 'int', 1)
-      5.08±0.8ms       2.63±0.1ms     0.52  stat_ops.FrameOps.time_op('skew', 'Int64', 0)
-      4.46±0.2ms       2.25±0.3ms     0.50  stat_ops.FrameOps.time_op('std', 'int', 1)
-      5.76±0.2ms       2.73±0.6ms     0.47  stat_ops.FrameOps.time_op('var', 'int', 1)
-      11.5±0.7ms       5.09±0.3ms     0.44  stat_ops.FrameOps.time_op('sem', 'int', 0)
-      1.36±0.2ms         600±50μs     0.44  stat_ops.FrameOps.time_op('prod', 'int', 0)
-        17.5±5ms       7.60±0.7ms     0.43  stat_ops.FrameOps.time_op('skew', 'int', 1)
-      8.64±0.7ms       3.37±0.4ms     0.39  stat_ops.FrameOps.time_op('kurt', 'int', 0)
-      9.03±0.6ms       3.21±0.2ms     0.36  stat_ops.FrameOps.time_op('skew', 'int', 0)
-      9.49±0.6ms       3.27±0.4ms     0.34  stat_ops.FrameOps.time_op('mad', 'int', 0)
-     1.31±0.08ms         420±20μs     0.32  stat_ops.FrameOps.time_op('sum', 'int', 1)
-      5.87±0.5ms       1.87±0.5ms     0.32  stat_ops.FrameOps.time_op('var', 'int', 0)
-     1.49±0.09ms         467±20μs     0.31  stat_ops.FrameOps.time_op('sum', 'int', 0)
-      4.44±0.3ms       1.39±0.1ms     0.31  stat_ops.FrameOps.time_op('std', 'int', 0)
-     1.90±0.06ms         569±70μs     0.30  stat_ops.FrameOps.time_op('mean', 'int', 0)
-      1.22±0.1ms          346±4μs     0.28  stat_ops.FrameOps.time_op('prod', 'int', 1)
-      1.86±0.1ms         519±60μs     0.28  stat_ops.FrameOps.time_op('mean', 'int', 1)
```

Will look to come up with a benchmark hitting this regression more squarely (which would help test if a more clever contiguity approach than always defaulting to "C" can improve performance).

EDIT: after regression is fixed, might be worth looking into reinstating something like the original approach. Some timings show performance benefit we lose here:

```
In [1]: import numpy as np

In [2]: import pandas as pd

In [3]: data = np.ones((2000, 2000), order="F")

In [4]: df = pd.DataFrame(data)

In [5]: %timeit df.astype(np.int32)
5.63 ms ± 114 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)  # this pr
5.7 ms ± 115 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)  # master

In [6]: data = np.ones((2000, 2000), order="C")

In [7]: df = pd.DataFrame(data)

In [8]: %timeit df.astype(np.int32)
26.8 ms ± 313 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)  # this pr
5.63 ms ± 77.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)  # master
```

EDIT: This slowdown comes down to the ravel copying because of contiguity that doesn't match. Let me know if it's better to try to fix the slowdown shown above in this pr or to leave as a followup for 1.4