Matmul: dispatch on specific blas paths using an enum

[jishnub]

This expands on the approach taken by #54552.

We pass on more type information to generic_matmatmul_wrapper!, which lets us convert the branches to method dispatches. This helps spread the latency around, so that instead of compiling all the branches in the first call, we now compile the branches only when they are actually taken. While this reduces the latency in individual branches, there is no reduction in latency if all the branches are reachable.

julia> A = rand(2,2);

julia> @time A * A;
  0.479805 seconds (809.66 k allocations: 40.764 MiB, 99.93% compilation time) # 1.12.0-DEV.806
  0.346739 seconds (633.17 k allocations: 31.320 MiB, 99.90% compilation time) # This PR

julia> @time A * A';
  0.030413 seconds (101.98 k allocations: 5.359 MiB, 98.54% compilation time) # v1.12.0-DEV.806
  0.148118 seconds (219.51 k allocations: 11.652 MiB, 99.72% compilation time) # This PR

The latency is spread between the two calls here.

In fresh sessions:

julia> A = rand(2,2);

julia> @time A * A';
  0.473630 seconds (825.65 k allocations: 41.554 MiB, 99.91% compilation time) # v1.12.0-DEV.806
  0.490305 seconds (774.87 k allocations: 38.824 MiB, 99.90% compilation time) # This PR

In this case, both the syrk and gemm branches are reachable, so there is no reduction in latency.

Analogously, there is a reduction in latency in the second set of matrix multiplications where we call symm!/hemm! or _generic_matmatmul:

julia> using LinearAlgebra

julia> A = rand(2,2);

julia> @time Symmetric(A) * A;
  0.711178 seconds (2.06 M allocations: 103.878 MiB, 2.20% gc time, 99.98% compilation time) # v1.12.0-DEV.806
  0.540669 seconds (904.12 k allocations: 43.576 MiB, 2.60% gc time, 97.36% compilation time) # This PR