feat(ebook): add protected phrase extraction library with config-driven tuning

Refactor protected phrase handling from a single module into a
python/ebook_search/protected_phrases package covering extraction,
storage, and runtime matching. Phrase filtering is now data-driven via
bundled TOML files: ignored_phrases, bad_starts, bad_ends, and
most_common_words.

Add phrase-tuning settings to EbookSearchConfig so candidate generation,
scoring, LLM judging, and matching are configurable rather than hardcoded:
token bounds, entity token limit, raw n-gram min count, frequency and
chapter-spread score thresholds, candidate/LLM/target caps, confidence
threshold, nesting defaults, and the phrase hit boost.

python/ebook_search/protected_phrases/bad_ends.toml

@@ -0,0 +1,30 @@
+tokens = [
+  "a",
+  "an",
+  "and",
+  "any",
+  "as",
+  "at",
+  "be",
+  "because",
+  "but",
+  "by",
+  "can",
+  "could",
+  "do",
+  "for",
+  "from",
+  "have",
+  "if",
+  "of",
+  "or",
+  "some",
+  "than",
+  "the",
+  "these",
+  "this",
+  "to",
+  "will",
+  "with",
+  "would",
+]