Add ADTypes sparsity detector

[gdalle]

Following the release of ADTypes.jl v1.0, this PR adds a SymbolicsSparsityDetector struct that implements the new sparsity pattern detection framework.

• Bump Symbolics version to 5.29.1
• Add dependency on ADTypes (imported by SciMLBase anyway)
• Define and export the SymbolicsSparsityDetector struct
• Implement ADTypes.jacobian_sparsity and ADTypes.hessian_sparsity for general arrays, not just vectors
• Add tests
• Add documentation

[gdalle]

@ChrisRackauckas this is part of the great wave of updates

[codecov-commenter]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 75.94%. Comparing base (79c4e92) to head (07fdabd).
Report is 198 commits behind head on master.

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Additional details and impacted files

@@            Coverage Diff             @@
##           master    #1134      +/-   ##
==========================================
- Coverage   77.07%   75.94%   -1.13%     
==========================================
  Files          32       36       +4     
  Lines        3533     3692     +159     
==========================================
+ Hits         2723     2804      +81     
- Misses        810      888      +78     

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[gdalle]

@Vaibhavdixit02 I think the NonLinearSolve upgrade needs to come first before this one. Let me know if you need help

[gdalle]

The only test failure that seems relevant is the downstream test with ModelingToolkit (https://github.com/JuliaSymbolics/Symbolics.jl/actions/runs/9127346950/job/25097459503?pr=1134).

I think it is happening because OptimizationBase has not yet fully embraced ADTypes v1.0 (see SciML/OptimizationBase.jl#44). @Vaibhavdixit02 what do you think?

[ChrisRackauckas]

Can you rebase onto the depwarn fixes?

[gdalle]

done

[gdalle]

Not sure that was a great idea though, nearly all tests fail on master now 🤔

[ChrisRackauckas]

@shashi is handling a big update over the next week, after which we will revisit this PR.

[gdalle]

No rush!

[gdalle]

Gentle bump on this one, it's absolutely non-destructive and would be useful for benchmarking