## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the investment in the wood industry.
- $x_2$ represents the investment in the bamboo industry.

## Step 2: Translate the natural language description into a symbolic representation
The objective is to maximize the average return, which can be represented as $0.07x_1 + 0.03x_2$.

## 3: Define the constraints
The constraints based on the problem description are:
1. $x_1 + x_2 \leq 10000$ (total investment not exceeding $10000)
2. $x_1 \geq 4x_2$ (investment in wood industry must be at least four times that in bamboo industry)
3. $x_1 \leq 7000$ (investment in wood industry not exceeding $7000)
4. $x_1 \geq 0$ and $x_2 \geq 0$ (investments cannot be negative)

## 4: Symbolic representation of the problem
The symbolic representation can be summarized as:
```json
{
'sym_variables': [('x1', 'investment in wood industry'), ('x2', 'investment in bamboo industry')],
'objective_function': '0.07*x1 + 0.03*x2',
'constraints': [
'x1 + x2 <= 10000',
'x1 >= 4*x2',
'x1 <= 7000',
'x1 >= 0',
'x2 >= 0'
]
}
```

## 5: Convert the problem into Gurobi code
Now, let's convert this into Gurobi code in Python:
```python
import gurobipy as gp

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

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

# Objective function: maximize 0.07*x1 + 0.03*x2
model.setObjective(0.07*x1 + 0.03*x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(x1 + x2 <= 10000, name="total_investment")
model.addConstr(x1 >= 4*x2, name="wood_bamboo_ratio")
model.addConstr(x1 <= 7000, name="wood_investment_limit")

# Solve the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal investment in wood industry: ${x1.varValue:.2f}")
    print(f"Optimal investment in bamboo industry: ${x2.varValue:.2f}")
    print(f"Optimal return: ${model.objVal:.2f}")
else:
    print("The model is infeasible.")
```