## Symbolic Representation

Let's denote the number of bracelets as $x_1$ and the number of rings as $x_2$. The objective is to maximize profit, which can be represented as $700x_1 + 300x_2$. The constraints based on the given information are:

- The team making bracelets can make at most 4 bracelets per day: $x_1 \leq 4$
- The team making rings can make at most 7 rings per day: $x_2 \leq 7$
- The master jeweler can check at most 30 jewels of either type per day: $x_1 + x_2 \leq 30$
- Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'bracelets'), ('x2', 'rings')],
    'objective_function': '700*x1 + 300*x2',
    'constraints': [
        'x1 <= 4',
        'x2 <= 7',
        'x1 + x2 <= 30',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(lb=0, ub=4, name="bracelets")
    x2 = model.addVar(lb=0, ub=7, name="rings")

    # Objective function: Maximize 700*x1 + 300*x2
    model.setObjective(700*x1 + 300*x2, gurobi.GRB.MAXIMIZE)

    # Additional constraint: x1 + x2 <= 30
    model.addConstr(x1 + x2 <= 30, name="jeweler_constraint")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"Bracelets: {x1.varValue}")
        print(f"Rings: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("The model is infeasible")

solve_jewelry_company_problem()
```