Vehicular Edge Computing (VEC) is a promising paradigm to support latency-sensitive and computation-intensive vehicular applications such as object detection, trajectory prediction, and hazard alerts. Microservice architectures enable scalability and modularity, but placing dependent microservices across heterogeneous edge service providers (ESPs) remains a critical challenge. Existing placement strategies often treat services independently, ignoring inter-service dependencies, which leads to increased inter-node communication delays and degraded performance. In this paper, we propose a dependency-aware microservice placement framework for VEC using deep reinforcement learning. Our approach leverages a Proximal Policy Optimization (PPO) based actor-critic agent to learn placement decisions that jointly minimize end-to-end service latency and maximize resource utilization while respecting capacity and SLA constraints. Experiments on real vehicular mobility traces from Luxembourg City demonstrate that the proposed method significantly reduces service latency and SLA violations compared to random and greedy baselines, while efficiently utilizing edge resources.