To solve Marty's investment problem, we need to maximize his return given the constraints on investing in the fishing and transportation industries. Let's denote:

- \(x\) as the amount invested in the fishing industry,
- \(y\) as the amount invested in the transportation industry.

The total budget is $20,000, so we have:
\[x + y \leq 20000\]

At least 40% of the money must be used in fishing, which translates to:
\[x \geq 0.4 \times 20000\]
\[x \geq 8000\]

At least $5,000 has to be invested in transportation:
\[y \geq 5000\]

The return from the fishing industry is $0.3 per dollar invested, and from the transportation industry is $0.15 per dollar invested. The total return \(R\) that Marty wants to maximize is given by:
\[R = 0.3x + 0.15y\]

We want to find the values of \(x\) and \(y\) that maximize \(R\) under the given constraints.

Here's how we can represent this problem in Gurobi using Python:

```python
from gurobipy import *

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

# Define variables
x = m.addVar(lb=8000, name="fishing_investment")  # At least $8,000 in fishing
y = m.addVar(lb=5000, name="transportation_investment")  # At least $5,000 in transportation

# Constraint: Total investment should not exceed $20,000
m.addConstr(x + y <= 20000, "total_budget")

# Objective function: Maximize return
m.setObjective(0.3*x + 0.15*y, GRB.MAXIMIZE)

# Solve the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f" Invest ${x.x} in fishing")
    print(f" Invest ${y.x} in transportation")
    print(f" Maximum return: ${m.objVal}")
else:
    print("No optimal solution found")
```

```python
from gurobipy import *

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

# Define variables
x = m.addVar(lb=8000, name="fishing_investment")  # At least $8,000 in fishing
y = m.addVar(lb=5000, name="transportation_investment")  # At least $5,000 in transportation

# Constraint: Total investment should not exceed $20,000
m.addConstr(x + y <= 20000, "total_budget")

# Objective function: Maximize return
m.setObjective(0.3*x + 0.15*y, GRB.MAXIMIZE)

# Solve the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f" Invest ${x.x} in fishing")
    print(f" Invest ${y.x} in transportation")
    print(f" Maximum return: ${m.objVal}")
else:
    print("No optimal solution found")
```