## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the number of bookcases
- $x_2$ represents the number of garden chairs

## Step 2: Translate the objective function into symbolic notation
The objective function is to maximize profit. Given that each bookcase yields a profit of $270 and each garden chair yields a profit of $350, the objective function can be written as:
\[ \text{Maximize:} \quad 270x_1 + 350x_2 \]

## 3: Translate the constraints into symbolic notation
The constraints based on the available resources are:
- Woodworking: $3x_1 + 2x_2 \leq 80$
- Nails: $2x_1 + 4x_2 \leq 70$
- Varnish: $3x_1 + 3x_2 \leq 90$
Also, $x_1 \geq 0$ and $x_2 \geq 0$ because the number of bookcases and garden chairs cannot be negative.

## 4: Write down the complete symbolic representation
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'bookcase'), ('x2', 'garden chair')],
'objective_function': '270*x1 + 350*x2',
'constraints': [
    '3*x1 + 2*x2 <= 80',
    '2*x1 + 4*x2 <= 70',
    '3*x1 + 3*x2 <= 90',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 5: Convert the symbolic representation into Gurobi code
Now, let's convert this into Gurobi code in Python:
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name='bookcase', lb=0, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name='garden chair', lb=0, ub=gurobi.GRB.INFINITY)

    # Set the objective function
    model.setObjective(270*x1 + 350*x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(3*x1 + 2*x2 <= 80, name='woodworking')
    model.addConstr(2*x1 + 4*x2 <= 70, name='nails')
    model.addConstr(3*x1 + 3*x2 <= 90, name='varnish')

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: Bookcases = {x1.varValue}, Garden Chairs = {x2.varValue}")
        print(f"Maximum profit: ${model.objVal:.2f}")
    else:
        print("The model is infeasible")

solve_optimization_problem()
```