## Step 1: Define the symbolic variables
Let's define the symbolic variables as follows:
- $x_1$ represents the amount invested in the oil and gas industry.
- $x_2$ represents the amount invested in the tech industry.
- $x_3$ represents the amount invested in the mining industry.
- $x_4$ represents the amount invested in the retail industry.

## Step 2: Formulate the objective function
The objective is to maximize the return on investment. The returns on investment for each industry are given as:
- Oil and gas: 6%
- Tech: 8%
- Mining: 9%
- Retail: 11%

The objective function can be written as: $0.06x_1 + 0.08x_2 + 0.09x_3 + 0.11x_4$.

## 3: Define the constraints
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: $x_4 \leq 0.28 \times 1000000$.

## 4: Convert the problem into a symbolic representation
```json
{
'sym_variables': [
    ('x1', 'oil and gas'),
    ('x2', 'tech'),
    ('x3', 'mining'),
    ('x4', 'retail')
],
'objective_function': '0.06x1 + 0.08x2 + 0.09x3 + 0.11x4',
'constraints': [
    'x1 + x2 + x3 + x4 <= 1000000',
    'x4 <= x1',
    'x2 <= x3',
    'x4 <= 280000'
]
}
```

## 5: Implement the problem in Gurobi
```python
import gurobi

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

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

    # Objective function
    model.setObjective(0.06 * x1 + 0.08 * x2 + 0.09 * x3 + 0.11 * x4, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 + x3 + x4 <= 1000000)
    model.addConstr(x4 <= x1)
    model.addConstr(x2 <= x3)
    model.addConstr(x4 <= 280000)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Oil and Gas: {x1.varValue}")
        print(f"Tech: {x2.varValue}")
        print(f"Mining: {x3.varValue}")
        print(f"Retail: {x4.varValue}")
        print(f"Max Return: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_investment_problem()
```