To solve Marty's investment problem, we first need to convert the natural language description into a symbolic representation. Let's denote the amount invested in the wood industry as $x_1$ and the amount invested in the bamboo industry as $x_2$.

The objective is to maximize the average return on investment. The total return can be calculated as $0.07x_1 + 0.03x_2$, and since the total investment is $10000, the average return is $\frac{0.07x_1 + 0.03x_2}{10000}$. However, to simplify the problem and avoid dealing with fractions, we can maximize the total return instead, which is $0.07x_1 + 0.03x_2$.

The constraints are:
1. The total investment is $10000: $x_1 + x_2 = 10000$
2. The investment in the wood industry must be at least four times as much as that in the bamboo industry: $x_1 \geq 4x_2$
3. The maximum investment in the wood industry is $7000: $x_1 \leq 7000$

Symbolic representation:
```json
{
  'sym_variables': [('x1', 'investment in the wood industry'), ('x2', 'investment in the bamboo industry')],
  'objective_function': '0.07*x1 + 0.03*x2',
  'constraints': ['x1 + x2 = 10000', 'x1 >= 4*x2', 'x1 <= 7000']
}
```

Now, let's implement this problem in Gurobi using Python:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, name="wood_investment")
x2 = m.addVar(lb=0, name="bamboo_investment")

# Set the objective function
m.setObjective(0.07*x1 + 0.03*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 == 10000, "total_investment")
m.addConstr(x1 >= 4*x2, "wood_vs_bamboo")
m.addConstr(x1 <= 7000, "max_wood_investment")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Wood investment: {x1.x}")
    print(f"Bamboo investment: {x2.x}")
else:
    print("No optimal solution found")
```