```json
{
  "sym_variables": [
    ("x1", "solar calculators"),
    ("x2", "finance calculators")
  ],
  "objective_function": "12*x1 + 9*x2",
  "constraints": [
    "5*x1 + 3*x2 <= 150",  // Silicon constraint
    "4*x1 + 5*x2 <= 150",  // Plastic constraint
    "2*x1 + 3*x2 <= 70",   // Steel constraint
    "x1 >= 0",
    "x2 >= 0"
  ]
}
```

```python
import gurobipy as gp

# Create a new model
model = gp.Model("Calculator Production")

# Create decision variables
solar = model.addVar(vtype=gp.GRB.CONTINUOUS, name="solar")
finance = model.addVar(vtype=gp.GRB.CONTINUOUS, name="finance")

# Set objective function
model.setObjective(12 * solar + 9 * finance, gp.GRB.MAXIMIZE)

# Add constraints
model.addConstr(5 * solar + 3 * finance <= 150, "silicon")
model.addConstr(4 * solar + 5 * finance <= 150, "plastic")
model.addConstr(2 * solar + 3 * finance <= 70, "steel")
model.addConstr(solar >= 0)
model.addConstr(finance >= 0)


# Optimize model
model.optimize()

# Print results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal profit: ${model.objVal:.2f}")
    print(f"Solar calculators: {solar.x:.2f}")
    print(f"Finance calculators: {finance.x:.2f}")
elif model.status == gp.GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization ended with status {model.status}")

```
