Reorganize NEON min/max implementation to be more generic

Thank you @seiko2plus for the excellent example.

Reorganized code so that it can be used for other architectures.  Core implementations and unroll factors should be the same as before for ARM NEON. Beyond reorganizing, we've added default implementations using universal intrinsics for non-ARM-NEON.  Additionally, we've moved most min, max, fmin, fmax implementations to a new dispatchable source file: numpy/core/src/umath/loops_minmax.dispatch.c.src

**Testing**
- Apple silicon M1 native (arm64 / aarch64) -- No test failures
- Apple silicon M1 Rosetta (x86_64) -- No new test failures
- iMacPro1,1 (AVX512F) -- No test failures

**Benchmarks**
- Apple silicon M1 native (arm64 / aarch64)
  - Similar improvements as before reorg (comparison below)

- x86_64 (both Apple silicon M1 Rosetta and iMacPro1,1 AVX512F)
  - Some x86_64 benchmarks are better, some are worse

Apple silicon M1 native (arm64 / aarch64) comparison to original implementation / before reorg:
```
       before           after         ratio
     [559ddede]       [a3463b09]
     <gh-issue-17989/improve-neon-min-max>       <gh-issue-17989/feedback/round-1>
+     6.45±0.04μs      7.07±0.09μs     1.10  bench_lib.Nan.time_nanargmin(200, 0.1)
+      32.1±0.3μs       35.2±0.2μs     1.10  bench_ufunc_strides.Unary.time_ufunc(<ufunc '_ones_like'>, 2, 1, 'd')
+     29.1±0.02μs      31.8±0.05μs     1.10  bench_core.Core.time_array_int_l1000
+      69.0±0.2μs         75.3±3μs     1.09  bench_ufunc_strides.Unary.time_ufunc(<ufunc 'logical_not'>, 2, 4, 'f')
+        92.0±1μs       99.5±0.5μs     1.08  bench_ufunc_strides.Unary.time_ufunc(<ufunc 'logical_not'>, 4, 4, 'd')
+      9.29±0.1μs       9.99±0.5μs     1.08  bench_ma.UFunc.time_1d(True, True, 10)
+       338±0.6μs         362±10μs     1.07  bench_function_base.Sort.time_sort('quick', 'int16', ('random',))
+     4.21±0.03μs       4.48±0.2μs     1.07  bench_core.CountNonzero.time_count_nonzero_multi_axis(3, 100, <class 'str'>)
+     12.3±0.06μs       13.1±0.7μs     1.06  bench_function_base.Median.time_even_small
+        1.27±0μs      1.35±0.06μs     1.06  bench_itemselection.PutMask.time_dense(False, 'float16')
+         139±1ns          147±6ns     1.06  bench_core.Core.time_array_1
+     33.7±0.01μs         35.5±2μs     1.05  bench_ufunc_strides.Unary.time_ufunc(<ufunc 'reciprocal'>, 2, 4, 'f')
+      69.4±0.1μs       73.1±0.2μs     1.05  bench_ufunc_strides.Unary.time_ufunc(<ufunc 'logical_not'>, 4, 4, 'f')
+      225±0.09μs          237±9μs     1.05  bench_random.Bounded.time_bounded('PCG64', [<class 'numpy.uint32'>, 2047])
-      15.7±0.5μs      14.9±0.03μs     0.95  bench_core.CountNonzero.time_count_nonzero_axis(2, 10000, <class 'numpy.int64'>)
-        34.2±2μs      32.0±0.03μs     0.94  bench_ufunc_strides.Unary.time_ufunc(<ufunc '_ones_like'>, 4, 2, 'f')
-     1.03±0.05ms          955±3μs     0.92  bench_lib.Nan.time_nanargmax(200000, 50.0)
-     6.97±0.08μs      6.43±0.02μs     0.92  bench_ma.UFunc.time_scalar(True, False, 10)
-        5.41±0μs      4.98±0.01μs     0.92  bench_ufunc_strides.AVX_cmplx_arithmetic.time_ufunc('subtract', 2, 'F')
-     22.4±0.01μs      20.6±0.02μs     0.92  bench_core.Core.time_array_float64_l1000
-     1.51±0.01ms         1.38±0ms     0.92  bench_core.CorrConv.time_correlate(1000, 10000, 'same')
-      10.1±0.2μs      9.27±0.09μs     0.92  bench_ufunc.UFunc.time_ufunc_types('invert')
-     8.50±0.02μs      7.80±0.09μs     0.92  bench_indexing.ScalarIndexing.time_assign_cast(1)
-      29.5±0.2μs      26.6±0.03μs     0.90  bench_ma.Concatenate.time_it('masked', 100)
-     2.09±0.02ms         1.87±0ms     0.90  bench_ma.UFunc.time_2d(True, True, 1000)
-        298±10μs        267±0.3μs     0.89  bench_app.MaxesOfDots.time_it
-      10.7±0.2μs      9.60±0.02μs     0.89  bench_ma.UFunc.time_1d(True, True, 100)
-         567±3μs          505±2μs     0.89  bench_lib.Nan.time_nanargmax(200000, 90.0)
-       342±0.9μs          282±5μs     0.83  bench_lib.Nan.time_nanargmax(200000, 2.0)
-       307±0.7μs        244±0.8μs     0.80  bench_lib.Nan.time_nanargmax(200000, 0.1)
-         309±1μs        241±0.1μs     0.78  bench_lib.Nan.time_nanargmax(200000, 0)
```

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