To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables for the quantities of action figures and toy cars to be produced, formulating the objective function that represents the total earnings, and listing the constraints based on the available production time and plastic.

Let's define:
- $x_1$ as the number of action figures to produce,
- $x_2$ as the number of toy cars to produce.

The objective function, which is to maximize the total earnings, can be represented as $2x_1 + 3x_2$, since each action figure earns $2 and each toy car earns $3.

The constraints are:
1. Production time constraint: $5x_1 + 8x_2 \leq 1000$ (since each action figure takes 5 minutes and each toy car takes 8 minutes, and there are 1000 minutes available).
2. Plastic cost constraint: $2x_1 + 2.5x_2 \leq 1000$ (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).
3. Non-negativity constraints: $x_1 \geq 0$ and $x_2 \geq 0$, because the number of action figures and toy cars produced cannot be negative.

Symbolic representation:
```json
{
  'sym_variables': [('x1', 'action figures'), ('x2', 'toy cars')],
  'objective_function': '2*x1 + 3*x2',
  'constraints': ['5*x1 + 8*x2 <= 1000', '2*x1 + 2.5*x2 <= 1000', 'x1 >= 0', 'x2 >= 0']
}
```

To solve this problem using Gurobi in Python, we will use the following code:

```python
from gurobipy import *

# Create a new model
m = Model("action_figures_and_toy_cars")

# Define variables
x1 = m.addVar(lb=0, vtype=GRB.INTEGER, name="action_figures")
x2 = m.addVar(lb=0, vtype=GRB.INTEGER, name="toy_cars")

# Set the objective function
m.setObjective(2*x1 + 3*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(5*x1 + 8*x2 <= 1000, "production_time")
m.addConstr(2*x1 + 2.5*x2 <= 1000, "plastic_cost")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Action figures to produce: {x1.x}")
    print(f"Toy cars to produce: {x2.x}")
    print(f"Maximum earnings: ${2*x1.x + 3*x2.x}")
else:
    print("No optimal solution found")
```