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

Let's denote:
- $x_1$ as the number of cans of Ruby paint
- $x_2$ as the number of cans of Sapphire paint

The objective is to minimize the cost, which is $12x_1 + 15x_2$.

The constraints based on the minimum requirements are:
- Dye: $2x_1 + 4x_2 \geq 15$
- Thinner: $4x_1 + 6x_2 \geq 20$
- Water: $5x_1 + 2x_2 \geq 18$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## Step 2: Convert the problem into a Gurobi-compatible format

The symbolic representation is:
```json
{
    'sym_variables': [('x1', 'cans of Ruby paint'), ('x2', 'cans of Sapphire paint')],
    'objective_function': '12*x1 + 15*x2',
    'constraints': [
        '2*x1 + 4*x2 >= 15',
        '4*x1 + 6*x2 >= 20',
        '5*x1 + 2*x2 >= 18',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Step 3: Write the Gurobi code in Python

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name='x1', lb=0, vtype=gurobi.GRB.CONTINUOUS)  # cans of Ruby paint
    x2 = model.addVar(name='x2', lb=0, vtype=gurobi.GRB.CONTINUOUS)  # cans of Sapphire paint

    # Objective function: minimize 12*x1 + 15*x2
    model.setObjective(12*x1 + 15*x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(2*x1 + 4*x2 >= 15, name='dye_constraint')
    model.addConstr(4*x1 + 6*x2 >= 20, name='thinner_constraint')
    model.addConstr(5*x1 + 2*x2 >= 18, name='water_constraint')

    # Optimize
    model.optimize()

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

solve_paint_mixture_problem()
```