feat(web): add abstraction for main correction-search algorithm, leveraged types 🚂

[jahorton]

Finishing up the batch of work started in #15814, this PR enacts new type abstractions that will let us search for context corrections in new ways.

As mentioned in #15814...

To accomplish the goal of abstracting the core search mechanisms, work will proceed in the following manner:

1. Relocate the code representing paths through the search graph (before making further changes).
   • refactor(web): move and rename SearchResult as TokenResultMapping 🚂 #15814 (this PR)
2. Properly return the type representing the critical metadata for processing partial + completed paths of the search graph.
   • change(web): return token-correction mapping from search-oriented methods 🚂 #15815
3. Prepare the search method for abstraction, extracting any code specific to the existing search pattern that may not work well when searching for corrections in alternative ways.
   • refactor(web): extract token-search result duplicate-filtering method 🚂 #15816
4. Abstractify the actual search method and critical metadata types.
   • feat(web): add abstraction for main correction-search algorithm, leveraged types 🚂 #15817

A new implementation of the abstract interfaces introduced here will come in #15818. Those changes will support correction-search over multiple tokens at once, allowing word-boundary corrections while prioritizing which token to search at any given time in support of completing a full phrase (possibly multiple tokens).

Build-bot: skip build:web
Test-bot: skip

[keymanapp-test-bot]

User Test Results

Test specification and instructions

User tests are not required

Test Artifacts

• Web
  • KeymanWeb Test Home - build : all tests passed (no artifacts on BuildLevel "build")

[jahorton]

Note that for correction-search processes, we need access to the following data for results:

• The object best representing the context-variation that led to the correction.
  • For tokens, that's SearchQuotientNode - each of which represents a given range of the context and possible edit sequences.
• The corrections themselves.
  • For tokens, that's SearchNode, which represents the specific edits made and tracks the LexicalModel object that provides efficient correction + prediction lookup operations.