As the usage of the edge-cloud continuum rises, Kubernetes presents itself as a solution that allows easy control and deployment of applications in these highly-distributed and heterogeneous environments. In this context, Artificial Intelligence methods have been proposed to aid in the task allocation process to optimize different aspects of the system, such as application execution time, load balancing or energy consumption. In this paper, we propose a space-time combinational model that uses Deep Reinforcement Learning (DRL) to recommend node allocations for Kubernetes pods with the objective of optimizing the overall energy consumption of the cluster while maintaining pod execution ratio. In particular, our approach uses Proximal Policy Optimization (PPO) with custom Neural Networks to train a DRL agent and includes a custom Kubernetes operator to enforce allocations based on the node recommendations generated by the agent. Using our custom solution, we performed a series of experiments with different workloads and compared the performance with the base Kubernetes scheduler. Our experimental results demonstrate a notable reduction of up to 24% in the energy consumption of the Kubernetes cluster.