To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. This involves defining variables for the quantities of SUVs and sedans to be produced, formulating the objective function based on the profit per vehicle, and establishing constraints related to the manufacturing line and testing time availability.

### Symbolic Representation

Let's define:
- \(x_1\) as the number of SUVs to be produced,
- \(x_2\) as the number of sedans to be 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 represented as:
\[ \text{Maximize:} \quad 10,000x_1 + 9,000x_2 \]

Constraints are based on the available time for manufacturing and testing:
- Manufacturing line constraint: \(200x_1 + 150x_2 \leq 20,000\),
- Testing constraint: \(120x_1 + 100x_2 \leq 10,000\).

Additionally, \(x_1 \geq 0\) and \(x_2 \geq 0\) because the number of vehicles cannot be negative.

### Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'Number of SUVs'), ('x2', 'Number of 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

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

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="SUVs", lb=0)
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="Sedans", lb=0)

# Set the objective function
m.setObjective(10000*x1 + 9000*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(200*x1 + 150*x2 <= 20000, "Manufacturing_Time")
m.addConstr(120*x1 + 100*x2 <= 10000, "Testing_Time")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Number of SUVs: {x1.x}")
    print(f"Number of Sedans: {x2.x}")
    print(f"Maximum Profit: ${10000*x1.x + 9000*x2.x:.2f}")
else:
    print("No optimal solution found")
```