## Step 1: Define the symbolic representation of the problem

Let's denote the money invested in the automotive industry as $x_1$ and the money invested in the textile industry as $x_2$. The problem description can be converted into a symbolic representation as follows:

- The objective is to maximize profit, which is $0.10x_1 + 0.08x_2$.
- The total investment is $30000, so $x_1 + x_2 \leq 30000$.
- The money invested in the automotive industry must be at least three times as much as the money invested in the textile industry, so $x_1 \geq 3x_2$.
- The money invested in the automotive industry must be at most $24000, so $x_1 \leq 24000$.
- Non-negativity constraints: $x_1 \geq 0$ and $x_2 \geq 0$.

## Step 2: Write down the symbolic representation in the required format

```json
{
    'sym_variables': [('x1', 'automotive industry investment'), ('x2', 'textile industry investment')],
    'objective_function': '0.10*x1 + 0.08*x2',
    'constraints': [
        'x1 + x2 <= 30000',
        'x1 >= 3*x2',
        'x1 <= 24000',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Step 3: Convert the symbolic representation into Gurobi code

```python
import gurobi

def solve_investment_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name='x1', lb=0)  # Investment in automotive industry
    x2 = model.addVar(name='x2', lb=0)  # Investment in textile industry

    # Objective function: maximize 0.10*x1 + 0.08*x2
    model.setObjective(x1 * 0.10 + x2 * 0.08, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 <= 30000)  # Total investment not exceeding $30000
    model.addConstr(x1 >= 3 * x2)      # Automotive investment at least three times textile investment
    model.addConstr(x1 <= 24000)       # Automotive investment not exceeding $24000

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal investment in automotive industry: ${x1.varValue:.2f}")
        print(f"Optimal investment in textile industry: ${x2.varValue:.2f}")
        print(f"Maximal profit: ${model.objVal:.2f}")
    else:
        print("The model is infeasible.")

solve_investment_problem()
```