[Feature] neural: forced-learn fast path and first-class freeze

[Feature] neural: forced-learn fast path and first-class freeze

Two training controls plus a supporting task primitive:

* train.forced_learn_minimal_scan (default on when disable_symbols_input):
  a high-priority neural prefilter disables every non-neural symbol on an
  ANN-Train scan, so a symbols-independent training vector is built without
  issuing RBL/DNS, fuzzy, bayes, ClickHouse or capture/cluster work. The
  stored vector and the profile key are byte-for-byte identical to the live
  full-scan path (asserted in tests). For symbol-dependent rules it stays off,
  and if any applicable neural rule needs symbols the whole task falls back to
  a full scan.

* train.frozen: stops automatic training and auto-storing of live vectors so a
  frozen model's pools cannot drift into an imbalanced live set, while
  inference keeps serving the current ANN unchanged. An explicit ANN-Train
  still stores and retrains on demand (gated by a per-profile retrain marker).
  Supersedes the auto-learn side of store_set_only/store_pool_only; both keep
  working when frozen is unset.

* task:disable_all_symbols([skip_mask]): Lua binding over the existing
  rspamd_symcache_disable_all_symbols "process only these" primitive (defaults
  to keeping explicit_disable symbols), used by the prefilter.

Functional coverage in test/functional/cases/330_neural/006_forced_learn_minimal
and 007_frozen.