## Symbolic Representation

To convert the given natural language description into a symbolic representation, let's define the variables and the objective function, as well as the constraints.

- **Variables:**
  - $x_1$ : Amount invested in the video game company
  - $x_2$ : Amount invested in the camera company
  - $x_3$ : Amount invested in the cell phone company
  - $x_4$ : Amount invested in the laptop company

## Objective Function and Constraints

- **Objective Function:** Maximize $0.07x_1 + 0.03x_2 + 0.09x_3 + 0.07x_4$
- **Constraints:**
  1. $x_1 + x_2 + x_3 + x_4 \leq 300000$ (Total investment not exceeding $300,000)
  2. $x_4 \leq x_1$ (Amount invested in laptop company cannot exceed the amount invested in the video game company)
  3. $x_2 \leq x_3$ (Amount invested in camera company cannot exceed the amount invested in the cell phone company)
  4. $x_4 \leq 0.15 \times 300000$ (At most 15% of total investment can be in the laptop company)
  5. $x_1, x_2, x_3, x_4 \geq 0$ (Non-negativity constraint, as investment cannot be negative)

## Symbolic Representation in JSON Format

```json
{
  'sym_variables': [
    ('x1', 'video game company'), 
    ('x2', 'camera company'), 
    ('x3', 'cell phone company'), 
    ('x4', 'laptop company')
  ], 
  'objective_function': '0.07*x1 + 0.03*x2 + 0.09*x3 + 0.07*x4', 
  'constraints': [
    'x1 + x2 + x3 + x4 <= 300000', 
    'x4 <= x1', 
    'x2 <= x3', 
    'x4 <= 45000', 
    'x1, x2, x3, x4 >= 0'
  ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

# Create a new model
model = gp.Model("Investment_Problem")

# Define variables
x1 = model.addVar(name="video_game_company", lb=0)
x2 = model.addVar(name="camera_company", lb=0)
x3 = model.addVar(name="cell_phone_company", lb=0)
x4 = model.addVar(name="laptop_company", lb=0)

# Objective function: Maximize returns
model.setObjective(0.07*x1 + 0.03*x2 + 0.09*x3 + 0.07*x4, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(x1 + x2 + x3 + x4 <= 300000, name="total_investment")
model.addConstr(x4 <= x1, name="laptop_vs_video_game")
model.addConstr(x2 <= x3, name="camera_vs_cell_phone")
model.addConstr(x4 <= 45000, name="laptop_limit")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
  print("Optimal Solution:")
  print(f"Video Game Company: ${x1.varValue:.2f}")
  print(f"Camera Company: ${x2.varValue:.2f}")
  print(f"Cell Phone Company: ${x3.varValue:.2f}")
  print(f"Laptop Company: ${x4.varValue:.2f}")
  print(f"Max Return: ${model.objVal:.2f}")
else:
  print("No optimal solution found.")
```