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/most_common_words.toml

@@ -0,0 +1,102 @@
+words = [
+    "the",
+    "be",
+    "to",
+    "of",
+    "and",
+    "a",
+    "in",
+    "that",
+    "have",
+    "I",
+    "it",
+    "for",
+    "not",
+    "on",
+    "with",
+    "he",
+    "as",
+    "you",
+    "do",
+    "at",
+    "this",
+    "but",
+    "his",
+    "by",
+    "from",
+    "they",
+    "we",
+    "say",
+    "her",
+    "she",
+    "or",
+    "an",
+    "will",
+    "my",
+    "one",
+    "all",
+    "would",
+    "there",
+    "their",
+    "what",
+    "so",
+    "up",
+    "out",
+    "if",
+    "about",
+    "who",
+    "get",
+    "which",
+    "go",
+    "me",
+    "when",
+    "make",
+    "can",
+    "like",
+    "time",
+    "no",
+    "just",
+    "him",
+    "know",
+    "take",
+    "people",
+    "into",
+    "year",
+    "your",
+    "good",
+    "some",
+    "could",
+    "them",
+    "see",
+    "other",
+    "than",
+    "then",
+    "now",
+    "look",
+    "only",
+    "come",
+    "its",
+    "over",
+    "think",
+    "also",
+    "back",
+    "after",
+    "use",
+    "two",
+    "how",
+    "our",
+    "work",
+    "first",
+    "well",
+    "way",
+    "even",
+    "new",
+    "want",
+    "because",
+    "any",
+    "these",
+    "give",
+    "day",
+    "most",
+    "us",
+]