To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. This involves defining variables and translating the objective function and constraints into algebraic terms.

Let's denote:
- \(x_1\) as the amount invested in the solar energy industry.
- \(x_2\) as the amount invested in the wind energy industry.

The objective is to maximize the return, which can be represented by the objective function:
\[ \text{Maximize: } 0.06x_1 + 0.05x_2 \]

Given constraints are:
1. The total investment is $50,000: \( x_1 + x_2 = 50000 \)
2. At least 70% of the investment must be in the wind energy industry: \( x_2 \geq 0.7 \times 50000 \)
3. At most 20% of the investment can be in the solar energy industry: \( x_1 \leq 0.2 \times 50000 \)

Symbolic representation:
```json
{
    'sym_variables': [('x1', 'amount invested in the solar energy industry'), 
                      ('x2', 'amount invested in the wind energy industry')],
    'objective_function': '0.06*x1 + 0.05*x2',
    'constraints': ['x1 + x2 = 50000', 'x2 >= 0.7*50000', 'x1 <= 0.2*50000']
}
```

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

```python
from gurobipy import *

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

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

# Set the objective function
m.setObjective(0.06*x1 + 0.05*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 == 50000, "total_investment")
m.addConstr(x2 >= 0.7*50000, "minimum_wind_energy_investment")
m.addConstr(x1 <= 0.2*50000, "maximum_solar_energy_investment")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Amount invested in solar energy: ${x1.x:.2f}")
    print(f"Amount invested in wind energy: ${x2.x:.2f}")
    print(f"Maximum return: ${m.objVal:.2f}")
else:
    print("No optimal solution found")
```