[Pyomo.DoE] Add simultaneous design of multiple experiments

[smondal13]

Fixes # .

Summary/Motivation:

This PR adds a new DesignOfExperiments.optimize_experiments() API in pyomo/contrib/doe/doe.py to support simultaneous optimization of multiple experiments in one workflow. The motivation is to provide a multi-experiment DoE interface with stronger initialization options, clearer mode handling (template vs. user-initialized experiments), and richer diagnostics/results than the existing single-experiment path.

Changes proposed in this PR:

• Added API optimize_experiments() for multi-experiment DoE optimization.
• Added Cholesky-based D- and A-optimality objective and Greybox-based D-, A-, E-, and ME- optimality objective
• Implemented two operating modes:
  • Template mode: pass one experiment and set n_exp.
  • User-initialized mode: pass a list of experiments; n_exp is inferred/validated.
• Added optional LHS-based initialization (initialization_method="lhs") with controls for:
  • sample count, seed, candidate evaluation parallelism, combination fim metric scoring parallelism,
  • worker count, chunk size, parallel threshold, and optional wall-clock budget.
• Added symmetry-breaking constraints for multi-experiment solves:
  • supports user-specified variable through sym_break_cons suffix,
  • falls back to the first experiment input with a diagnostic warning when not provided.
• Expanded output for this API:
  • per-scenario and per-experiment results (designs, outputs, measurement errors, FIM/sensitivities),
  • aggregated FIM metrics, timings, settings, names, diagnostics, and structured run_info.
• Added JSON-safe serialization via _DoEResultsJSONEncoder for numpy/Pyomo-enum values when writing results_file.

Note:

• Added new documentation.md which describes the API. This documentation is to help the reviewers to understand the API and will not be merged into Pyomo:main

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[smondal13]

NO NEED TO REVIEW THIS SCRIPT

[smondal13]

@blnicho , I think the current imports will have deferred imports, right? I have removed the if scipy_availble: statement and moved those imports inside the method

[smondal13]

@adowling2 @slilonfe5 I have removed the LHS parallel initialization code from optimize_experiments() in doe.py. Feel free to review it.

@blnicho, considering the May 15 release, we have decided to remove the LHS parallel initialization code that we had. I have also implemented the deferred import. Please review at your earliest convenience.