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perf: bulk fast paths for NTuple-from-array and rand!(::Array{Complex}) (#61762)

I pointed Claude at [perf.julialang.org](https://perf.julialang.org/)
and asked it to find opportunities to improve unusually slow benchmarks.

See
[JuliaCI/julia-ci-timing@main/analysis](https://github.com/JuliaCI/julia-ci-timing/tree/main/analysis)
for some of the scripts it used.

This is what it came up with:

----

Two small, BaseBenchmarks-driven perf fixes that share a theme — when
the
storage of an array already has the layout you want, dispatch to the
bulk
path on a `reinterpret` view instead of going through the per-element
fallback.

### `NTuple{N,E}(::Union{Array,Memory})` for `N >= 32`

The default `_totuple(::Type{All32{E,N}}, itr)` path collects into an
intermediate `Vector` and splats. The `All32` cap exists so we don't
specialize tuple construction on every `N`; this PR keeps that property
(no per-`N` codegen) by adding a single fast path for `Array`/`Memory`
inputs whose `isbits` element type matches the requested tuple element
type. In that case the storage is layout-identical to the tuple, so a
single `reinterpret` view + scalar load returns the tuple in O(1) (plus
a length check).

The fast path lives in `reinterpretarray.jl` because `reinterpret` on
arrays isn't yet defined when `tuple.jl` is loaded. Over-long inputs are
silently truncated to match the iterator-based fallback. Non-matching
eltypes / non-isbits eltypes fall through to the existing path
unchanged.

```
                                            master       this PR
NTuple{40,Float64}(::Vector{Float64})        1.36 μs      7.3 ns
NTuple{150,Float64}(::Vector{Float64})       4.92 μs     17.0 ns
```

(0 allocations on the fast path, vs. `N+~3` allocations and `~33 N` B
on master.)

### Bulk `rand!(::Array{Complex{T}})` for `T <: HWReal`

`rand!(::AbstractRNG, ::Array{Complex{T}})` previously fell through to
the generic `AbstractArray` path — two scalar `rand` calls per element.
A packed... (continued)
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