Vehicular Edge Computing (VEC) has emerged as a promising solution for offloading computation-intensive tasks from vehicles to nearby edge servers, enabling low-latency and energy-efficient services. However, real-world vehicular workloads are often heterogeneous, varying significantly in data size, deadline sensitivity, and task priority. Such heterogeneities introduce significant challenges in effective task scheduling and resource allocation. In this paper, we propose a novel digital twin-assisted offloading method based on a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework, named DT-MAP. This method jointly considers task heterogeneity, vehicular mobility, and priority-aware scheduling. Additionally, the proposed method incorporates a dynamic reward shaping mechanism that accounts for task priority, delay sensitivity and load penalty, enabling agents to learn cooperative offloading policies under constrained edge resources. To evaluate our approach, we develop a simulated VEC environment inspired by digital twins, which dynamically reflects vehicle mobility, network conditions, and edge server status. Experiments with varying vehicle numbers show that DT-MAP outperforms baseline strategies (Random, Greedy, SAC), with an average load balancing improvement of 17.48%, 14.81%, and 3.78%, respectively. It also reduces average latency by 9.57%, 7.30%, and 3.24%. Additionally, DT-MAP achieves near-saturation resource utilization, with an average efficiency of 98.70%.