To solve Marty's investment problem, we first need to convert the natural language description into a symbolic representation. This involves defining variables and translating the objective and constraints into mathematical expressions.

Let's define:
- $x_1$ as the amount invested in the fishing industry.
- $x_2$ as the amount invested in the transportation industry.

The objective function is to maximize the total return, which can be represented as $0.3x_1 + 0.15x_2$, since the fishing industry yields a $0.3 return per dollar and the transportation industry yields a $0.15 return per dollar.

The constraints are:
1. The total amount invested cannot exceed $20,000: $x_1 + x_2 \leq 20000$.
2. At least 40% of the money must be used in fishing: $x_1 \geq 0.4 \times 20000$ or $x_1 \geq 8000$.
3. At least $5,000 has to be invested in transportation: $x_2 \geq 5000$.
4. Non-negativity constraints: $x_1 \geq 0$ and $x_2 \geq 0$, since the amount invested cannot be negative.

Thus, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'amount invested in fishing'), ('x2', 'amount invested in transportation')],
    'objective_function': '0.3*x1 + 0.15*x2',
    'constraints': ['x1 + x2 <= 20000', 'x1 >= 8000', 'x2 >= 5000', 'x1 >= 0', 'x2 >= 0']
}
```

To find the solution using Gurobi in Python, we'll write the following code:

```python
from gurobipy import *

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

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

# Set the objective function
m.setObjective(0.3*x1 + 0.15*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 <= 20000, name="Total_Investment")
m.addConstr(x1 >= 8000, name="Fishing_Minimum")
m.addConstr(x2 >= 5000, name="Transportation_Minimum")

# Optimize the model
m.optimize()

# Print the results
for v in m.getVars():
    print(f"{v.varName}: {v.x}")
print(f"Objective: {m.objVal}")
```