[Feature] Add expression-based autolearn for neural LLM providers

[Feature] Add expression-based autolearn for neural LLM providers

Add integrated autolearn system for neural networks with LLM providers:

- New lua_neural_learn library with guards system and rspamd_expression
  support for complex conditions
- Expression-based conditions: spam_condition, ham_condition using
  rspamd_expression syntax (e.g., "BAYES_SPAM & DMARC_POLICY_REJECT")
- Score, action, and symbol-based thresholds
- Pluggable guards via rspamd_plugins['neural'].autolearn hooks
- Mempool-based flag passing (no double scanning)
- Probabilistic sampling for training volume control

Also includes contrib/neural-embedding-service with a FastEmbed-based
Python service for CPU-optimized embedding inference, compatible with
both Ollama and OpenAI API formats.

Configuration example:
  autolearn {
    enabled = true;
    spam_score = 15.0;
    spam_condition = "BAYES_SPAM & (DMARC_POLICY_REJECT | RBL_SPAMHAUS)";
    ham_condition = "BAYES_HAM & DKIM_VALID_AU & SPF_PASS";
  }