## Symbolic Representation

To solve the given problem, we first need to convert the natural language description into a symbolic representation. Let's denote:

- \(x_1\) as the number of reams of dotted paper
- \(x_2\) as the number of reams of grid paper

The objective is to maximize profit. The profit per ream of dotted paper is $5.5, and per ream of grid paper is $11. Therefore, the objective function can be represented as:

\[ \text{Maximize:} \quad 5.5x_1 + 11x_2 \]

The constraints are based on the availability of the cutting and printing machines:

- The cutting machine is available for 3000 minutes. Given that a ream of dotted paper requires 3 minutes and a ream of grid paper requires 1.5 minutes on the cutting machine, we have:
\[ 3x_1 + 1.5x_2 \leq 3000 \]

- The printing machine is also available for 3000 minutes. Given that a ream of dotted paper requires 5.5 minutes and a ream of grid paper requires 7 minutes on the printing machine, we have:
\[ 5.5x_1 + 7x_2 \leq 3000 \]

Additionally, \(x_1 \geq 0\) and \(x_2 \geq 0\) because the number of reams cannot be negative.

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'reams of dotted paper'), ('x2', 'reams of grid paper')],
    'objective_function': '5.5*x1 + 11*x2',
    'constraints': [
        '3*x1 + 1.5*x2 <= 3000',
        '5.5*x1 + 7*x2 <= 3000',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="dotted_paper", lb=0, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name="grid_paper", lb=0, ub=gurobi.GRB.INFINITY)

    # Define objective function
    model.setObjective(5.5 * x1 + 11 * x2, gurobi.GRB.MAXIMIZE)

    # Define constraints
    model.addConstr(3 * x1 + 1.5 * x2 <= 3000, name="cutting_machine_constraint")
    model.addConstr(5.5 * x1 + 7 * x2 <= 3000, name="printing_machine_constraint")

    # Optimize model
    model.optimize()

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

solve_optimization_problem()
```