## Step 1: Define the symbolic variables and the objective function
Let's define the symbolic variables as follows:
- $x_1$ represents the amount invested in GPUs
- $x_2$ represents the amount invested in CPUs
- $x_3$ represents the amount invested in software
- $x_4$ represents the amount invested in mobile devices

The objective function to maximize the return is: $0.04x_1 + 0.06x_2 + 0.11x_3 + 0.08x_4$

## Step 2: Define the constraints
The constraints based on the problem description are:
1. $x_1 \leq x_2$ (The amount invested in GPUs cannot exceed the amount invested in CPUs)
2. $x_4 \leq x_3$ (The amount invested in mobile devices cannot exceed the amount invested in software)
3. $x_1 \leq 0.09 \times 2000000$ (At most 9% of the investment can be in GPUs)
4. $x_1 + x_2 + x_3 + x_4 \leq 2000000$ (The total investment cannot exceed $2000000)

## 3: Convert the problem into a Gurobi-compatible format
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'GPUs'), ('x2', 'CPUs'), ('x3', 'software'), ('x4', 'mobile devices')],
'objective_function': '0.04*x1 + 0.06*x2 + 0.11*x3 + 0.08*x4',
'constraints': [
    'x1 - x2 <= 0',
    'x4 - x3 <= 0',
    'x1 - 0.09*2000000 <= 0',
    'x1 + x2 + x3 + x4 - 2000000 <= 0',
    'x1, x2, x3, x4 >= 0'
]
}
```

## 4: Write the Gurobi code in Python
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name='GPUs', lb=0)
    x2 = model.addVar(name='CPUs', lb=0)
    x3 = model.addVar(name='software', lb=0)
    x4 = model.addVar(name='mobile devices', lb=0)

    # Define the objective function
    model.setObjective(0.04 * x1 + 0.06 * x2 + 0.11 * x3 + 0.08 * x4, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(x1 <= x2)
    model.addConstr(x4 <= x3)
    model.addConstr(x1 <= 0.09 * 2000000)
    model.addConstr(x1 + x2 + x3 + x4 <= 2000000)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Optimal investment:')
        print(f'GPUs: {x1.varValue}')
        print(f'CPUs: {x2.varValue}')
        print(f'software: {x3.varValue}')
        print(f'mobile devices: {x4.varValue}')
        print(f'Optimal return: {model.objVal}')
    else:
        print('No optimal solution found')

solve_investment_problem()
```