## Step 1: Define the symbolic representation of the problem

Let's denote the number of phones to be repaired as $x_1$ and the number of laptops to be repaired as $x_2$. The profit per phone is $50, and the profit per laptop is $60. The time required for inspection and fixing of each phone and laptop is given.

## Step 2: Formulate the objective function

The objective function to maximize profit is: $50x_1 + 60x_2$.

## 3: Formulate the constraints

Each phone requires 20 minutes of inspection, and each laptop requires 30 minutes of inspection. The total available time for inspection is 6000 minutes. This gives the constraint: $20x_1 + 30x_2 \leq 6000$.

Each phone requires 30 minutes of fixing, and each laptop requires 50 minutes of fixing. The total available time for fixing is 7000 minutes. This gives the constraint: $30x_1 + 50x_2 \leq 7000$.

Additionally, $x_1 \geq 0$ and $x_2 \geq 0$ because the number of phones and laptops to be repaired cannot be negative.

## 4: Symbolic representation in the required format

```json
{
    'sym_variables': [('x1', 'phones'), ('x2', 'laptops')], 
    'objective_function': '50*x1 + 60*x2', 
    'constraints': [
        '20*x1 + 30*x2 <= 6000', 
        '30*x1 + 50*x2 <= 7000', 
        'x1 >= 0', 
        'x2 >= 0'
    ]
}
```

## Step 5: Convert the problem into Gurobi code

```python
import gurobipy as gp

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

# Define the variables
x1 = model.addVar(name="phones", lb=0, vtype=gp.GRB.INTEGER)  # Number of phones
x2 = model.addVar(name="laptops", lb=0, vtype=gp.GRB.INTEGER)  # Number of laptops

# Define the objective function
model.setObjective(50*x1 + 60*x2, gp.GRB.MAXIMIZE)

# Add the constraints
model.addConstr(20*x1 + 30*x2 <= 6000, name="inspection_time")
model.addConstr(30*x1 + 50*x2 <= 7000, name="fixing_time")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal solution: phones = {x1.varValue}, laptops = {x2.varValue}")
    print(f"Maximum profit: ${50*x1.varValue + 60*x2.varValue}")
else:
    print("The model is infeasible")
```