Mooncake overlay for erroring out before Mooncake.DerivedRule construction

[AstitvaAggarwal]

Closes SciML/SciMLSensitivity.jl#1230 and chalk-lab/Mooncake.jl#587.

This PR handles cases where:
DiffEqBase._concrete_solve_adjoint must error out when using ReverseDiffAdjoint/TrackerAdjoint while differentiating via Mooncake. This error was already handled by SciMLSensitivity but was not getting hit.
Therefore calling Mooncake.@mooncake_overlay for DiffEqBase.set_mooncakeoriginator_if_mooncake is required otherwise the Mooncake.DerivedRule which contains primal typechecks fails: as Tracker for example adds tags such as Tracker.TrackerReal{Float64} around Float64's to the forward pass primals.

The previous PR handled:
any other case (eg: not using ReverseDiffAdjoint/TrackerAdjoint) when it is required to use a Mooncake.rrule!! for DiffEqBase.set_mooncakeoriginator_if_mooncake in a Mooncake.DerivedRule.

julia> using OrdinaryDiffEq, SciMLSensitivity, Mooncake, Test
       mooncake_gradient(f, x) = Mooncake.value_and_gradient!!(Mooncake.build_rrule(f, x), f, x)[2][2]

       odef(du, u, p, t) = du .= u .* p
       const prob = ODEProblem(odef, [2.0], (0.0, 1.0), [3.0])

       struct senseloss{T}
           sense::T
       end
       function (f::senseloss)(u0p)
           sum(solve(prob, Tsit5(), u0 = u0p[1:1], p = u0p[2:2], abstol = 1e-12,
               reltol = 1e-12, saveat = 0.1, sensealg = f.sense))
       end
       function loss(u0p)
           sum(solve(prob, Tsit5(), u0 = u0p[1:1], p = u0p[2:2], abstol = 1e-12, reltol = 1e-12,
               saveat = 0.1))
       end
       u0p = [2.0, 3.0]
WARNING: redefinition of constant Main.prob. This may fail, cause incorrect answers, or produce other errors.

julia> @test_throws SciMLSensitivity.MooncakeTrackedRealError mooncake_gradient(senseloss(TrackerAdjoint()), u0p) ≈ dup
Test Passed
      Thrown: SciMLSensitivity.MooncakeTrackedRealError