## Step 1: Define the symbolic representation of the problem
Let's denote the amount invested in the fishing industry as $x_1$ and the amount invested in the transportation industry as $x_2$. The objective is to maximize the return, which is $0.3x_1 + 0.15x_2$. The constraints are:
- $x_1 + x_2 \leq 20000$ (total investment not exceeding $20000)
- $x_1 \geq 0.4 \times 20000 = 8000$ (at least 40% of the money invested in fishing)
- $x_2 \geq 5000$ (at least $5000 invested in transportation)
- $x_1, x_2 \geq 0$ (non-negativity constraint)

## Step 2: Express the problem in the required symbolic format
The symbolic variables are:
- $x_1$ for the amount invested in the fishing industry
- $x_2$ for the amount invested in the transportation industry

The objective function is: $0.3x_1 + 0.15x_2$

The constraints are:
- $x_1 + x_2 \leq 20000$
- $x_1 \geq 8000$
- $x_2 \geq 5000$
- $x_1, x_2 \geq 0$

In the required format:
```json
{
'sym_variables': [('x1', 'fishing industry investment'), ('x2', 'transportation industry investment')],
'objective_function': '0.3*x1 + 0.15*x2',
'constraints': ['x1 + x2 <= 20000', 'x1 >= 8000', 'x2 >= 5000', 'x1 >= 0', 'x2 >= 0']
}
```

## Step 3: Convert the problem into Gurobi code
Now, let's implement this problem using Gurobi in Python:
```python
import gurobi

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

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

    # Objective function: maximize 0.3*x1 + 0.15*x2
    model.setObjective(0.3 * x1 + 0.15 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 <= 20000, name="total_investment")
    model.addConstr(x1 >= 8000, name="fishing_investment_min")
    model.addConstr(x2 >= 5000, name="transportation_investment_min")

    # Optimize the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal investment in fishing industry: ", x1.varValue)
        print("Optimal investment in transportation industry: ", x2.varValue)
        print("Maximum return: ", model.objVal)
    else:
        print("The model is infeasible")

solve_investment_problem()
```