## Symbolic Representation

To solve the given problem, we first need to convert the natural language description into a symbolic representation. Let's define the variables and the objective function, as well as the constraints.

- Let \(x_1\) be the number of SUVs produced.
- Let \(x_2\) be the number of sedans produced.

The objective is to maximize profit. Given that the profit per SUV is $10,000 and per sedan is $9,000, the objective function can be written as:

\[ \text{Maximize:} \quad 10000x_1 + 9000x_2 \]

The constraints are based on the time available for manufacturing and testing:

1. Manufacturing line time: \(200x_1 + 150x_2 \leq 20000\)
2. Testing time: \(120x_1 + 100x_2 \leq 10000\)
3. Non-negativity: \(x_1 \geq 0, x_2 \geq 0\)

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'SUVs'), ('x2', 'sedans')],
    'objective_function': '10000*x1 + 9000*x2',
    'constraints': [
        '200*x1 + 150*x2 <= 20000',
        '120*x1 + 100*x2 <= 10000',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

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

# Define variables
x1 = model.addVar(name="SUVs", lb=0, vtype=gp.GRB.INTEGER)  # Number of SUVs
x2 = model.addVar(name="sedans", lb=0, vtype=gp.GRB.INTEGER)  # Number of sedans

# Objective function: Maximize profit
model.setObjective(10000*x1 + 9000*x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(200*x1 + 150*x2 <= 20000, name="Manufacturing_Time")
model.addConstr(120*x1 + 100*x2 <= 10000, name="Testing_Time")

# Solve the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal solution: SUVs = {x1.varValue}, sedans = {x2.varValue}")
    print(f"Max Profit: ${model.objVal}")
else:
    print("No optimal solution found.")
```