## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the number of phones produced
- $x_2$ represents the number of laptops produced

## Step 2: Formulate the objective function
The profit per phone is $300 and the profit per laptop is $500. The objective function to maximize the company's profit is:
\[ \text{Maximize:} \quad 300x_1 + 500x_2 \]

## 3: Define the constraints
1. Silicon chip availability: Each phone requires 2 silicon chips, and each laptop requires 4 silicon chips. The company can buy 200 silicon chips per week.
\[ 2x_1 + 4x_2 \leq 200 \]
2. Engineering time availability: Each phone requires 5 hours, and each laptop requires 6 hours. The company has 250 hours available.
\[ 5x_1 + 6x_2 \leq 250 \]
3. Assembly time availability: Each phone requires 3 hours, and each laptop requires 1 hour. The company has 300 hours available.
\[ 3x_1 + x_2 \leq 300 \]
4. Minimum production requirements: The company wants to produce at least 30 units of phones and at least 30 units of laptops.
\[ x_1 \geq 30 \]
\[ x_2 \geq 30 \]
5. Non-negativity constraints: The number of phones and laptops produced cannot be negative.
\[ x_1 \geq 0 \]
\[ x_2 \geq 0 \]
However, since $x_1 \geq 30$ and $x_2 \geq 30$ already imply $x_1, x_2 \geq 0$, the non-negativity constraints are implicitly satisfied.

## 4: Symbolic Representation
```json
{
    'sym_variables': [('x1', 'phones'), ('x2', 'laptops')],
    'objective_function': '300*x1 + 500*x2',
    'constraints': [
        '2*x1 + 4*x2 <= 200',
        '5*x1 + 6*x2 <= 250',
        '3*x1 + x2 <= 300',
        'x1 >= 30',
        'x2 >= 30'
    ]
}
```

## 5: Gurobi Code
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(lb=30, name="phones")  # at least 30 phones
    x2 = model.addVar(lb=30, name="laptops")  # at least 30 laptops

    # Objective function: Maximize profit
    model.setObjective(300*x1 + 500*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(2*x1 + 4*x2 <= 200, name="silicon_chips")
    model.addConstr(5*x1 + 6*x2 <= 250, name="engineering_time")
    model.addConstr(3*x1 + x2 <= 300, name="assembly_time")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Phones: {x1.varValue}")
        print(f"Laptops: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```