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

Let's denote the number of pairs of earrings as $x_1$ and the number of watches as $x_2$. The objective is to maximize the profit $P = 45x_1 + 70x_2$.

## Step 2: Identify the constraints

The heating machine is available for at most 14 hours, and it takes 2 hours to make a pair of earrings and 3.5 hours to make a watch. This gives us the constraint $2x_1 + 3.5x_2 \leq 14$.

The polishing machine is available for at most 10 hours, and it takes 1.5 hours to make a pair of earrings and 2 hours to make a watch. This gives us the constraint $1.5x_1 + 2x_2 \leq 10$.

## Step 3: Non-negativity constraints

Since the store cannot produce a negative number of earrings or watches, we have $x_1 \geq 0$ and $x_2 \geq 0$.

## 4: Symbolic representation

The symbolic representation of the problem is:

```json
{
    'sym_variables': [('x1', 'pairs of earrings'), ('x2', 'watches')],
    'objective_function': '45*x1 + 70*x2',
    'constraints': [
        '2*x1 + 3.5*x2 <= 14',
        '1.5*x1 + 2*x2 <= 10',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 5: Gurobi code

Now, let's implement this problem using Gurobi in Python:

```python
import gurobi as gp

# Create a new model
m = gp.Model()

# Define the variables
x1 = m.addVar(name="earrings", lb=0)
x2 = m.addVar(name="watches", lb=0)

# Define the objective function
m.setObjective(45 * x1 + 70 * x2, gp.GRB.MAXIMIZE)

# Define the constraints
m.addConstr(2 * x1 + 3.5 * x2 <= 14, name="heating_machine")
m.addConstr(1.5 * x1 + 2 * x2 <= 10, name="polishing_machine")

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution:")
    print(f"Pairs of earrings: {x1.varValue}")
    print(f"Watches: {x2.varValue}")
    print(f"Max profit: {m.objVal}")
else:
    print("No optimal solution found")
```