GaussNewton returns unstable when problem is converged

[vpuri3]

julia> sol = solve(NonlinearLeastSquaresProblem((x, p) -> x, ones(3)), GaussNewton())
u: 3-element Vector{Float64}:
 0.0
 0.0
 0.0

julia> sol.retcode
ReturnCode.Success = 1

julia> sol = solve(NonlinearLeastSquaresProblem((x, p) -> x, zeros(3)), GaussNewton())
u: 3-element Vector{Float64}:
 0.0
 0.0
 0.0

julia> sol.retcode
ReturnCode.Unstable = 6

[avik-pal]

https://github.com/SciML/DiffEqBase.jl/blob/8c0db9efb1a3907e3abb596545474621986b36fc/src/termination_conditions.jl#L290 line needs to be > else if the initial point is the solution, we hit protective termination