The surge of artificial intelligence (AI) has intensified compute-intensive tasks, sharply increasing the need for energy-efficient management in geo-distributed data centers. Existing approaches struggle to coordinate task scheduling and cooling control due to mismatched time constants, stochastic Information Technology (IT) workloads, variable renewable energy, and fluctuating electricity prices. To address these challenges, we propose D3T, a dual-timescale deep reinforcement learning (DRL) framework that jointly optimizes task scheduling and thermal management for energy-efficient geo-distributed data centers. At the fast timescale, D3T employs Deep Q-Network (DQN) to schedule tasks, reducing operational expenditure (OPEX) and task sojourn time. At the slow timescale, a QMIX-based multi-agent DRL method regulates cooling across distributed data centers by dynamically adjusting airflow rates, thereby preventing hotspots and reducing energy waste. Extensive experiments were conducted using TRNSYS with real-world traces, and the results demonstrate that, compared to baseline algorithms, D3T reduces OPEX by 13% in IT subsystems and 29% in cooling subsystems, improves power usage effectiveness (PUE) by 7%, and maintains more stable thermal safety across geo-distributed data centers.