## Step 1: Define the symbolic representation of the problem
Let's denote the number of action figures as $x_1$ and the number of toy cars as $x_2$. The objective is to maximize earnings, which is $2x_1 + 3x_2$. The constraints are:
- Production time: $5x_1 + 8x_2 \leq 1000$
- Plastic cost: $2x_1 + 2.5x_2 \leq 1000$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## 2: Convert the problem into a symbolic representation
The symbolic representation can be written as:
```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'
]
}
```

## 3: Translate the symbolic representation into Gurobi code
To solve this linear programming problem using Gurobi, we will use the Gurobi Python API.

```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x1 = model.addVar(lb=0, name="action_figures")
x2 = model.addVar(lb=0, name="toy_cars")

# Define the objective function
model.setObjective(2*x1 + 3*x2, gurobi.GRB.MAXIMIZE)

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

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Action figures: {x1.varValue}")
    print(f"Toy cars: {x2.varValue}")
    print(f"Earnings: {2*x1.varValue + 3*x2.varValue}")
else:
    print("No optimal solution found.")
```