Multi-scenario and multi-task recommendation systems efficiently facilitate knowledge transfer across different scenarios and tasks. However, many existing approaches inadequately incorporate personalized information across users and scenarios. Moreover, the conversion rate (CVR) task in multi-task learning often encounters challenges like sample selection bias, resulting from systematic differences between the training and inference sample spaces, and data sparsity due to infrequent clicks. To address these issues, we propose Adaptive Entire-space Multi-scenario Multi-task Transfer Learning model (AEM$^{2}$TL) with four key modules: 1) Scenario-CGC (Scenario-Customized Gate Control), 2) Task-CGC (Task-Customized Gate Control), 3) Personalized Gating Network, and 4) Entire-space Supervised Multi-Task Module. AEM$^{2}$TL employs a multi-gate mechanism to effectively integrate shared and specific information across scenarios and tasks, enhancing prediction adaptability. To further improve task-specific personalization, it incorporates personalized prior features and applies a gating mechanism that dynamically scales the top-layer neural units. A novel post-impression behavior decomposition technique is designed to leverage all impression samples across the entire space, mitigating sample selection bias and data sparsity. Furthermore, an adaptive weighting mechanism dynamically allocates attention to tasks based on their relative importance, ensuring optimal task prioritization. Extensive experiments on one industrial and two real-world public datasets indicate the superiority of AEM$^{2}$TL over state-of-the-art methods.