You are given a pandas DataFrame 'pins' with columns [pin_id:int, category_id:int, time_spent_sec:float, pin_format:string] and a dict 'category_map' mapping category_id -> category_name. Write Python to return a tuple (category_name, avg_time) for the category with the highest average time_spent_sec among rows where pin_format == 'video'. Requirements: exclude rows with null/NaN category_id or nonpositive time; break ties by lexicographically smallest category_name; time complexity O(n), extra space O(k) where k is distinct categories; round avg_time to two decimals. Sample input: 'pins' pin_id | category_id | time_spent_sec | pin_format 1 | 10 | 12 | video 2 | 10 | 20 | static 3 | 11 | 30 | video 4 | null | 25 | video 5 | 11 | 0 | video category_map = {10: "Food", 11: "Travel"} Expected output on sample: ("Travel", 30.00).