To solve this optimization problem, we need to define the decision variables, the objective function, and the constraints. Let's denote the number of action figures produced as \(x\) and the number of toy cars produced as \(y\).

- **Decision Variables**: \(x \geq 0\) (number of action figures), \(y \geq 0\) (number of toy cars)
- **Objective Function**: Maximize earnings, which is given by \(2x + 3y\), since each action figure earns $2 and each toy car earns $3.
- **Constraints**:
  - Production time constraint: \(5x + 8y \leq 1000\) (since each action figure takes 5 minutes and each toy car takes 8 minutes, and there are 1000 minutes available).
  - Plastic cost constraint: \(2x + 2.50y \leq 1000\) (since each action figure costs $2 worth of plastic and each toy car costs $2.50 worth of plastic, and there is $1000 worth of plastic available).

Given these definitions, the problem can be solved using linear programming techniques, which are directly implementable in Gurobi.

```python
from gurobipy import *

# Create a model
m = Model("Action_Figures_and_Toy_Cars")

# Define decision variables
x = m.addVar(name="action_figures", lb=0)
y = m.addVar(name="toy_cars", lb=0)

# Set the objective function to maximize earnings
m.setObjective(2*x + 3*y, GRB.MAXIMIZE)

# Add production time constraint
m.addConstr(5*x + 8*y <= 1000, name="production_time")

# Add plastic cost constraint
m.addConstr(2*x + 2.50*y <= 1000, name="plastic_cost")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print(f"Optimal solution: x = {x.x}, y = {y.x}")
    print(f"Maximum earnings: {2*x.x + 3*y.x}")
else:
    print("No optimal solution found")
```