Update ExaModels and MadNLP

[pulsipher]

This tests out the ExaModels changes proposed in exanauts/ExaModels.jl#256.

This now updates the versioning of ExaModels and MadNLP with the new versions.

[codecov-commenter]

Codecov Report

❌ Patch coverage is 91.66667% with 6 lines in your changes missing coverage. Please review.
✅ Project coverage is 85.71%. Comparing base (64c85bb) to head (8f50be1).
⚠️ Report is 1 commits behind head on main.

Files with missing lines	Patch %	Lines
src/transform.jl	91.66%	6 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main      #31      +/-   ##
==========================================
+ Coverage   85.09%   85.71%   +0.62%     
==========================================
  Files           6        6              
  Lines         711      735      +24     
==========================================
+ Hits          605      630      +25     
+ Misses        106      105       -1     

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[pulsipher]

This appears to make very little difference for performance and allocations/memory. Using the quadrotor example from our benchmarks with 1000 time points, I get the following with the current release:

using NLPModelsIpopt, InfiniteOpt, InfiniteExaModels

m = quad(num_supports = 1000, backend = ExaTranscriptionBackend(IpoptSolver))
set_silent(m)
@time build_transcription_backend!(m) # builds the ExaModel
@time optimize!(m)

0.168895 seconds (450.06 k allocations: 24.030 MiB)
0.865922 seconds (310 allocations: 2.626 MiB)

With this PR, I get:

0.174419 seconds (449.24 k allocations: 24.468 MiB)
0.856932 seconds (310 allocations: 2.629 MiB)

[pulsipher]

This is broken because of exanauts/ExaModels.jl#269. Previously, we could add constraints without a generator by directly giving an ExaModels expression and an iterator, but that method was removed.

[pulsipher]

Requires exanauts/ExaModels.jl#286.