Refactor discrete_mechanisms to Select-Measure-Estimate base class

[copybara-service]

Refactor discrete_mechanisms to Select-Measure-Estimate base class

Introduce a DiscreteMechanism base class in base.py that implements the
Select-Measure-Estimate paradigm from McKenna et al. (2021). Each step of
the pipeline is a separate overridable method, enabling code reuse across
mechanisms that differ primarily in the select step.

New abstractions:

• DiscreteMechanism: Base dataclass with configure() for budget setup.
  __call__ owns the fixed pre/post scaffolding (measure one-way, compress,
  generate, package result); _run is the overridable middle where the
  estimation strategy lives. One-shot mechanisms override only _select;
  adaptive mechanisms override _run.
• GaussianMarginalMeasurement: A DPMechanism that encapsulates
  Gaussian noise addition to marginal queries with proper dp_event.
• _one_way_cliques hook: Subclasses can restrict which one-way
  marginals are measured (AIM/AIM-GDP use workload-specific columns).

Mechanism migrations:

• Independent: one_way_budget_fraction=1.0, _select -> [], delegates dp_event
• Direct: one_way_budget_fraction=0.0, _select -> prespecified queries
• MST: _select delegates to existing exponential mechanism helper
• AIM: Overrides _run for the adaptive annealed loop, reuses building blocks
• AIM-GDP: Same _run pattern as AIM but in GDP budget space
• SWIFT: Overrides _select, _precompile, _measure, _estimate, _generate
  for per-clique budget allocation and junction tree precompilation