## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the amount invested in the solar energy industry.
- $x_2$ represents the amount invested in the wind energy industry.

## Step 2: Translate the natural language description into a symbolic representation
The objective is to maximize the return on investment. The return from the solar energy industry is 6% of $x_1$, and the return from the wind energy industry is 5% of $x_2$. Therefore, the objective function to maximize is $0.06x_1 + 0.05x_2$.

## 3: Define the constraints
1. Total investment constraint: $x_1 + x_2 \leq 50000$ (since we have $50000 available to invest).
2. Wind energy industry constraint: $x_2 \geq 0.7 \times 50000$ (at least 70% of the investment should be in the wind energy industry).
3. Solar energy industry constraint: $x_1 \leq 0.2 \times 50000$ (at most 20% of the investment can be in the solar energy industry).
4. Non-negativity constraints: $x_1 \geq 0$ and $x_2 \geq 0$ (investments cannot be negative).

## 4: Write down the symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'solar energy industry investment'), ('x2', 'wind energy industry investment')],
'objective_function': '0.06*x1 + 0.05*x2',
'constraints': [
    'x1 + x2 <= 50000',
    'x2 >= 0.7 * 50000',
    'x1 <= 0.2 * 50000',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 5: Convert the problem into Gurobi code
```python
import gurobi

def solve_investment_problem():
    # Create a new model
    model = gurobi.Model()

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

    # Objective function: maximize 0.06*x1 + 0.05*x2
    model.setObjective(0.06 * x1 + 0.05 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 <= 50000, name="total_investment")
    model.addConstr(x2 >= 0.7 * 50000, name="wind_energy_constraint")
    model.addConstr(x1 <= 0.2 * 50000, name="solar_energy_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal investment in solar energy industry: ", x1.varValue)
        print("Optimal investment in wind energy industry: ", x2.varValue)
        print("Maximum return: ", model.objVal)
    else:
        print("The model is infeasible")

solve_investment_problem()
```