To solve the given optimization problem, we first need to translate the natural language description into a mathematical formulation. The goal is to maximize returns from investing $300,000 in four tech companies with different return on investments (ROIs) and under certain constraints.

Let's denote:
- \(x_1\) as the amount invested in the video game company,
- \(x_2\) as the amount invested in the camera company,
- \(x_3\) as the amount invested in the cell phone company, and
- \(x_4\) as the amount invested in the laptop company.

Given ROIs are:
- Video game company: 7% or 0.07,
- Camera company: 3% or 0.03,
- Cell phone company: 9% or 0.09,
- Laptop company: 7% or 0.07.

The objective function to maximize the total return is:
\[ \text{Maximize} \quad 0.07x_1 + 0.03x_2 + 0.09x_3 + 0.07x_4 \]

Constraints are:
1. Total investment: \( x_1 + x_2 + x_3 + x_4 = 300,000 \)
2. Laptop investment cannot exceed video game investment: \( x_4 \leq x_1 \)
3. Camera investment cannot exceed cell phone investment: \( x_2 \leq x_3 \)
4. At most 15% of total investment can be in the laptop company: \( x_4 \leq 0.15 \times 300,000 = 45,000 \)

All investments must be non-negative.

Now, let's formulate this problem in Gurobi Python code:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, name="Video_Game_Company")
x2 = m.addVar(lb=0, name="Camera_Company")
x3 = m.addVar(lb=0, name="Cell_Phone_Company")
x4 = m.addVar(lb=0, name="Laptop_Company")

# Objective function
m.setObjective(0.07*x1 + 0.03*x2 + 0.09*x3 + 0.07*x4, GRB.MAXIMIZE)

# Constraints
m.addConstr(x1 + x2 + x3 + x4 == 300000, name="Total_Investment")
m.addConstr(x4 <= x1, name="Laptop_vs_Video_Game")
m.addConstr(x2 <= x3, name="Camera_vs_Cell_Phone")
m.addConstr(x4 <= 45000, name="Max_Laptop_Investment")

# Optimize model
m.optimize()

# Print results
for v in m.getVars():
    print(f"{v.varName}: {v.x}")

print(f"Total Return: {m.objVal}")
```