Here's the formulation of the linear program and the Gurobi code to solve it:

**Decision Variables:**

* `x`: Investment in the wood industry (in dollars)
* `y`: Investment in the bamboo industry (in dollars)

**Objective Function:**

Maximize total return:  `0.07x + 0.03y`

**Constraints:**

* **Total Investment:** `x + y <= 10000`
* **Wood Investment Multiple:** `x >= 4y`
* **Maximum Wood Investment:** `x <= 7000`
* **Non-negativity:** `x >= 0`, `y >= 0`


```python
import gurobipy as gp
from gurobipy import GRB

# Create a new model
m = gp.Model("Investment_Optimization")

# Create decision variables
x = m.addVar(nonnegative=True, name="wood_investment")
y = m.addVar(nonnegative=True, name="bamboo_investment")

# Set objective function
m.setObjective(0.07 * x + 0.03 * y, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x + y <= 10000, "total_investment")
m.addConstr(x >= 4 * y, "wood_multiple")
m.addConstr(x <= 7000, "max_wood")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print(f"Optimal investment in wood: ${x.x:.2f}")
    print(f"Optimal investment in bamboo: ${y.x:.2f}")
    print(f"Maximum average return: ${m.objVal:.2f}")
else:
    print("No solution found.")

```
