Fix bug in initialization routines to return solve with bounded nlp

[stephenscini]

Fixes # .

Fixes issue in initialization routines where the model is run through nlp solver before bounds are removed.

Summary/Motivation:

Currently: Original variables are bounded in methods and then there is cleanup but the initial point from lp, pwl, and global approximations is not verified to work with nlp_solver

Changes proposed in this PR:

• After each method "Finally: _cleanup()", run the original model again through the nlp solver and return that result.
• Additional warning added for using the global solver without specifying a time limit.

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[stephenscini]

@michaelbynum Please review when available.

[michaelbynum]

This is almost perfect. One comment below. Also, the code for the final nlp solve is almost identical in all three methods. Could you move this to a helper function?

[stephenscini]

@michaelbynum Addressed comments. Please take another look when available. Thanks!

[michaelbynum]

Thanks!

[codecov]

Codecov Report

❌ Patch coverage is 86.95652% with 3 lines in your changes missing coverage. Please review.
✅ Project coverage is 89.94%. Comparing base (f8ed23c) to head (f7c9e73).

Files with missing lines	Patch %	Lines
pyomo/devel/initialization/initialize.py	81.25%	3 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #3991      +/-   ##
==========================================
- Coverage   89.94%   89.94%   -0.01%     
==========================================
  Files         917      917              
  Lines      109096   109104       +8     
==========================================
+ Hits        98128    98132       +4     
- Misses      10968    10972       +4     

Flag	Coverage Δ
builders	29.08% <4.34%> (-0.01%)	⬇️
default	86.14% <69.56%> (?)
expensive	35.08% <4.34%> (?)
linux	87.67% <86.95%> (-1.98%)	⬇️
linux_other	87.67% <86.95%> (-0.01%)	⬇️
oldsolvers	27.56% <4.34%> (+<0.01%)	⬆️
osx	83.12% <86.95%> (+<0.01%)	⬆️
win	85.42% <86.95%> (-0.01%)	⬇️

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