Cloud computing has become a pivotal platform for executing scientific workflows due to its scalable and cost-effective infrastructure. Scientific Cloud Service Providers (SCSPs) act as intermediaries that rent virtual machines (VMs) from Infrastructure-as-a-Service (IaaS) providers to meet users' workflow execution demands. The SCSP earns profit from the execution of scientific workflows if it completes the execution of the workflow before the specified deadline of the workflow. This paper addresses two key challenges that impact the profitability of SCSPs: the cold start problem and the efficient management of diverse VM pricing models, namely reserved, on-demand, and spot instances. We propose a hybrid scheduling framework that integrates initial planning based on historical data with real-time adaptations informed by actual workload variations. In the initial phase, VMs are provisioned using reserved pricing based on predicted workloads and spot instances. During execution, the system dynamically adjusts by provisioning additional VMs through on-demand or spot instances to accommodate unexpected bursts in task arrivals. Our framework also incorporates a dependency-aware task scheduling strategy that accounts for cold start delays and spot pricing volatility. Experimental results on real-world benchmark datasets demonstrate that our approach outperforms state-of-the-art methods, achieving up to 20% improvement over cold-start-focused techniques and 15% over pricing-model-based VM provisioning strategies.