hprod/jprod not GPU-compatible

See the following tests:
- [ ] https://github.com/JuliaSmoothOptimizers/ADNLPModels.jl/blob/53a494c6e19177d326275fb027f6732f985ade9a/test/nls/nlpmodelstest.jl#L42
- [ ] https://github.com/JuliaSmoothOptimizers/ADNLPModels.jl/blob/53a494c6e19177d326275fb027f6732f985ade9a/test/nlp/nlpmodelstest.jl#L25
- [ ] https://github.com/JuliaSmoothOptimizers/ADNLPModels.jl/blob/53a494c6e19177d326275fb027f6732f985ade9a/test/gpu.jl#L28
- [ ] https://github.com/JuliaSmoothOptimizers/ADNLPModels.jl/blob/53a494c6e19177d326275fb027f6732f985ade9a/test/gpu.jl#L18
A MWE:
```julia
using CUDA, ADNLPModels, NLPModels

hs6_autodiff(::Type{T}; kwargs...) where {T <: Number} = hs6_autodiff(Vector{T}; kwargs...)
function hs6_autodiff(::Type{S} = Vector{Float64}; kwargs...) where {S}
x0 = S([-12 // 10; 1])
f(x) = (1 - x[1])^2
c(x) = [10 * (x[2] - x[1]^2)]
lcon = fill!(S(undef, 1), 0)
ucon = fill!(S(undef, 1), 0)

return ADNLPModel(f, x0, c, lcon, ucon, name = "hs6_autodiff"; kwargs...)
end

nlp = hs6_autodiff(CuArray{Float64})
CUDA.allowscalar()
jth_hprod(nlp, nlp.meta.x0, nlp.meta.x0, 1) # same for hprod(nlp, nlp.meta.x0, nlp.meta.x0)
#=
ERROR: GPU compilation of MethodInstance for (::GPUArrays.var"#map_kernel#38"{…})(::CUDA.CuKernelContext, ::CuDeviceVector{…}, ::Base.Broadcast.Broadcasted{…}, ::Int64) failed
KernelError: passing and using non-bitstype argument

Argument 4 to your kernel function is of type Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1}, Tuple{Base.OneTo{Int64}}, ForwardDiff.var"#85#86"{ForwardDiff.Tag{ADNLPModels.var"#lag#141"{Int64, var"#f#6", Int64, ADNLPModels.var"#c!#319"{var"#c#7"}, ADNLPModels.var"#lag#134#142"}, Float64}}, Tuple{Base.Broadcast.Extruded{Vector{ForwardDiff.Dual{ForwardDiff.Tag{ADNLPModels.var"#lag#141"{Int64, var"#f#6", Int64, ADNLPModels.var"#c!#319"{var"#c#7"}, ADNLPModels.var"#lag#134#142"}, Float64}, Float64, 1}}, Tuple{Bool}, Tuple{Int64}}}}, which is not isbits:
.args is of type Tuple{Base.Broadcast.Extruded{Vector{ForwardDiff.Dual{ForwardDiff.Tag{ADNLPModels.var"#lag#141"{Int64, var"#f#6", Int64, ADNLPModels.var"#c!#319"{var"#c#7"}, ADNLPModels.var"#lag#134#142"}, Float64}, Float64, 1}}, Tuple{Bool}, Tuple{Int64}}} which is not isbits.
.1 is of type Base.Broadcast.Extruded{Vector{ForwardDiff.Dual{ForwardDiff.Tag{ADNLPModels.var"#lag#141"{Int64, var"#f#6", Int64, ADNLPModels.var"#c!#319"{var"#c#7"}, ADNLPModels.var"#lag#134#142"}, Float64}, Float64, 1}}, Tuple{Bool}, Tuple{Int64}} which is not isbits.
.x is of type Vector{ForwardDiff.Dual{ForwardDiff.Tag{ADNLPModels.var"#lag#141"{Int64, var"#f#6", Int64, ADNLPModels.var"#c!#319"{var"#c#7"}, ADNLPModels.var"#lag#134#142"}, Float64}, Float64, 1}} which is not isbits.
=#
```