To solve George's investment problem, we first need to translate the natural language description into a symbolic representation. Let's define the variables as follows:

- $x_1$: The amount invested in the oil and gas industry.
- $x_2$: The amount invested in the tech industry.
- $x_3$: The amount invested in the mining industry.
- $x_4$: The amount invested in the retail industry.

The objective is to maximize the return on investment. Given the returns are 6% for oil and gas, 8% for tech, 9% for mining, and 11% for retail, the objective function can be written as:

Maximize: $0.06x_1 + 0.08x_2 + 0.09x_3 + 0.11x_4$

The constraints are:
1. The total amount invested cannot exceed $1,000,000: $x_1 + x_2 + x_3 + x_4 \leq 1000000$
2. The amount invested in the retail industry cannot exceed the amount invested in the oil and gas industry: $x_4 \leq x_1$
3. The amount invested in the tech industry cannot exceed the amount invested in the mining industry: $x_2 \leq x_3$
4. At most 28% of the total amount invested can be in the retail industry: $x_4 \leq 0.28(x_1 + x_2 + x_3 + x_4)$

This translates to the following symbolic representation:
```json
{
  'sym_variables': [('x1', 'oil and gas'), ('x2', 'tech'), ('x3', 'mining'), ('x4', 'retail')],
  'objective_function': 'Maximize: 0.06*x1 + 0.08*x2 + 0.09*x3 + 0.11*x4',
  'constraints': [
    'x1 + x2 + x3 + x4 <= 1000000',
    'x4 <= x1',
    'x2 <= x3',
    'x4 <= 0.28*(x1 + x2 + x3 + x4)'
  ]
}
```

Now, let's implement this problem in Gurobi using Python:
```python
from gurobipy import *

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

# Define the variables
x1 = m.addVar(name="oil_and_gas", lb=0)
x2 = m.addVar(name="tech", lb=0)
x3 = m.addVar(name="mining", lb=0)
x4 = m.addVar(name="retail", lb=0)

# Set the objective function
m.setObjective(0.06*x1 + 0.08*x2 + 0.09*x3 + 0.11*x4, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 + x3 + x4 <= 1000000, name="total_investment")
m.addConstr(x4 <= x1, name="retail_vs_oil_gas")
m.addConstr(x2 <= x3, name="tech_vs_mining")
m.addConstr(x4 <= 0.28*(x1 + x2 + x3 + x4), name="max_retail_percentage")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Oil and Gas: {x1.x}")
    print(f"Tech: {x2.x}")
    print(f"Mining: {x3.x}")
    print(f"Retail: {x4.x}")
    print(f"Total Return: {m.objVal}")
else:
    print("No optimal solution found")
```