Dividing diagonal by triangular matrix returns sparse matrix

Depending on what types are used, dividing a diagonal matrix by a triangular matrix returns a sparse matrix, even though the result is triangular. I think it would make a lot more sense to return a matrix with a triangular type instead.
```julia
julia> VERSION
v"1.3.0-rc1.0"

julia> n = 3;

julia> L = rand(n, n);

julia> D = rand(n);

julia> tril(L)\diagm(D)
3×3 Array{Float64,2}:
0.0666276 0.0 0.0
-0.115247 1.0267 0.0
0.0406685 -0.656616 0.507579

julia> LowerTriangular(L)\diagm(D)
3×3 Array{Float64,2}:
0.0666276 0.0 0.0
-0.115247 1.0267 0.0
0.0406685 -0.656616 0.507579

julia> tril(L)\Diagonal(D)
3×3 SparseArrays.SparseMatrixCSC{Float64,Int64} with 6 stored entries:
[1, 1] = 0.0666276
[2, 1] = -0.115247
[3, 1] = 0.0406685
[2, 2] = 1.0267
[3, 2] = -0.656616
[3, 3] = 0.507579

julia> LowerTriangular(L)\Diagonal(D)
3×3 SparseArrays.SparseMatrixCSC{Float64,Int64} with 6 stored entries:
[1, 1] = 0.0666276
[2, 1] = -0.115247
[3, 1] = 0.0406685
[2, 2] = 1.0267
[3, 2] = -0.656616
[3, 3] = 0.507579
```

It's very similar to what was addressed here: JuliaLang/julia#27999

Start by reproducing the provided Julia examples for triangular and diagonal matrix division, comparing the result types. Trace the dispatch used by triangular-matrix solve with Diagonal and add or update coverage so the result retains a triangular type rather than becoming sparse.

Written by the indexing model from the issue text.