Identifying influential nodes is crucial in social network analysis. Existing methods often neglect the tendencies of local opinion leaders, resulting in overlapping influence regions among seed nodes. Furthermore, in dynamic scenarios with non-uniform influence propagation intensity, approaches based on vanilla graph neural networks (GNNs) struggle to effectively model and aggregate node influence characteristics during message passing. Current techniques also fail to adequately address the multilayer nature of social networks and node heterogeneity. To address these issues, this study proposes Inf-MDE, a novel multilayer influence maximization method leveraging differentiated graph embeddings, which models social relationships using a multilayer network structure. The model further extracts latent influence propagation subgraphs of nodes to eliminate the representation bias between node embeddings and propagation dynamics. In addition, Inf-MDE incorporates an adaptive local influence aggregation mechanism within its GNN design. This mechanism dynamically adjusts the aggregation strategy of influence features during message passing based on local context and influence intensity, thus effectively capturing inter-layer propagation heterogeneity and intra-layer diffusion dynamics. Extensive experiments conducted on four different multilayer social network datasets demonstrate that Inf-MDE significantly outperforms existing baseline methods. The source code of this study is available at https://github.com/lyao972/Inf-MDE.