## Step 1: Define the symbolic representation of the problem
Let's denote the number of rings as $x_1$ and the number of necklaces as $x_2$. The objective is to maximize profit, which is $50x_1 + 75x_2$. The constraints are:
- $2x_1 + 3x_2 \leq 1000$ (gold units constraint)
- $x_1 \geq 3x_2$ (rings to necklaces ratio constraint)
- $x_2 \geq 50$ (minimum necklaces constraint)
- $x_1, x_2 \geq 0$ (non-negativity constraint)

## 2: Express the problem in the required symbolic format
The symbolic variables are:
- $x_1$ for rings
- $x_2$ for necklaces

The objective function is: $50x_1 + 75x_2$

The constraints are:
- $2x_1 + 3x_2 \leq 1000$
- $x_1 - 3x_2 \geq 0$
- $x_2 \geq 50$
- $x_1, x_2 \geq 0$

In the required format:
```json
{
'sym_variables': [('x1', 'rings'), ('x2', 'necklaces')],
'objective_function': '50*x1 + 75*x2',
'constraints': [
    '2*x1 + 3*x2 <= 1000',
    'x1 - 3*x2 >= 0',
    'x2 >= 50',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 3: Convert the problem into Gurobi code
Now, let's implement this problem using Gurobi in Python:
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="rings", lb=0, vtype=gurobi.GRB.INTEGER)  # Number of rings
    x2 = model.addVar(name="necklaces", lb=0, vtype=gurobi.GRB.INTEGER)  # Number of necklaces

    # Objective function: Maximize 50*x1 + 75*x2
    model.setObjective(50*x1 + 75*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(2*x1 + 3*x2 <= 1000, name="gold_constraint")
    model.addConstr(x1 - 3*x2 >= 0, name="ratio_constraint")
    model.addConstr(x2 >= 50, name="necklaces_min_constraint")

    # Solve the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: rings = {x1.varValue}, necklaces = {x2.varValue}")
    else:
        print("No optimal solution found")

solve_jewelry_problem()
```