In wireless networks, the effectiveness of beamforming and power allocation strategies is crucial in meeting the increasing data demands of users and ensuring rapid data transmission. Graph learning approaches have been developed to tackle complex challenges in wireless communications and have shown promising results. However, most existing graph learning methods primarily focus on node features, neglecting the potential benefits of leveraging rich information from edge features. This study addresses this limitation and proposes a novel framework called Heterogeneous Node and Edge Graph Neural Network (HNENN). Specifically designed for heterogeneous networks, HNENN incorporates node-level and edge-level attention layers to learn and aggregate node and edge embeddings. The alternating stacking of these two layers facilitates the mutual enhancement of node and edge embeddings. Simulations show that the proposed framework works better than state-of-the-art approaches, getting a higher sum rate in different scenarios with different numbers of D2D pairs, training samples, interference levels, and transmit power budgets.