To solve George's investment problem, we need to formulate a linear programming (LP) model that maximizes his return on investment while adhering to the given constraints. Let's denote the amount invested in each industry as follows:
- $x_1$: Amount invested in the oil and gas industry.
- $x_2$: Amount invested in the tech industry.
- $x_3$: Amount invested in the mining industry.
- $x_4$: Amount invested in the retail industry.

The objective function aims to maximize the total return on investment, given the returns for each industry:
- Oil and Gas: 6%
- Tech: 8%
- Mining: 9%
- Retail: 11%

Thus, the objective function can be written as:
\[ \text{Maximize} \quad 0.06x_1 + 0.08x_2 + 0.09x_3 + 0.11x_4 \]

The constraints are as follows:
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.
\[ x_4 \leq 0.28(x_1 + x_2 + x_3 + x_4) \]
5. Non-negativity constraints, as the amount invested in each industry cannot be negative:
\[ x_1, x_2, x_3, x_4 \geq 0 \]

Now, let's translate this LP formulation into Gurobi code in Python:

```python
from gurobipy import *

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

# 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_Percent")

# 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")
```