Testing Differentiation Interfase and check efficient boost using Mooncake

[facusapienza21]

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[facusapienza21]

I open this PR to check whether MoonCake can be use to compute the gradients more efficiently in SphereUDE. With this core, the benchmark for the following adjoint methods give the following results:

sensealg_types = [
    GaussAdjoint(autojacvec = ReverseDiffVJP(true)),
    InterpolatingAdjoint(autojacvec = ReverseDiffVJP(true)),
    QuadratureAdjoint(autojacvec = ReverseDiffVJP(true)),
    GaussAdjoint(autojacvec = MooncakeVJP()),
    InterpolatingAdjoint(autojacvec = MooncakeVJP()),
    QuadratureAdjoint(autojacvec = MooncakeVJP()),
    SphereBackSolveAdjoint()
]

I don't observe any incredible increase in efficiency due to Mooncake, but also I am not sure I am using right. All this benchmarks are done without any regularization, so nested AD is not really required.