## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the number of sets of general purpose vessels
- $x_2$ represents the number of sets of pharmaceutical vessels
- $x_3$ represents the number of sets of pressure vessels

## Step 2: Formulate the objective function
The profit per set from selling the vessels are $2000, $3000, and $4500 for the general purpose, pharmaceutical, and pressure vessels respectively. The objective function to maximize profit is:
\[ \text{Maximize:} \quad 2000x_1 + 3000x_2 + 4500x_3 \]

## 3: Define the constraints
The company produces 120 tons of iron ore and 70 tons of zinc ore each month. The requirements for each type of vessel are:
- General purpose vessels: 3.5 tons of iron ore and 2 tons of zinc ore
- Pharmaceutical vessels: 4 tons of iron ore and 5 tons of zinc ore
- Pressure vessels: 2 tons of iron ore and 3.5 tons of zinc ore

The constraints based on the available resources are:
\[ 3.5x_1 + 4x_2 + 2x_3 \leq 120 \]
\[ 2x_1 + 5x_2 + 3.5x_3 \leq 70 \]
Also, $x_1, x_2, x_3 \geq 0$ because the number of vessels cannot be negative.

## 4: Symbolic Representation
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'general purpose vessels'), ('x2', 'pharmaceutical vessels'), ('x3', 'pressure vessels')],
'objective_function': '2000*x1 + 3000*x2 + 4500*x3',
'constraints': ['3.5*x1 + 4*x2 + 2*x3 <= 120', '2*x1 + 5*x2 + 3.5*x3 <= 70', 'x1 >= 0', 'x2 >= 0', 'x3 >= 0']
}
```

## 5: Gurobi Code
Now, let's implement this problem using Gurobi in Python:
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="general_purpose_vessels", lb=0, vtype=gurobi.GRB.CONTINUOUS)
    x2 = model.addVar(name="pharmaceutical_vessels", lb=0, vtype=gurobi.GRB.CONTINUOUS)
    x3 = model.addVar(name="pressure_vessels", lb=0, vtype=gurobi.GRB.CONTINUOUS)

    # Define the objective function
    model.setObjective(2000*x1 + 3000*x2 + 4500*x3, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(3.5*x1 + 4*x2 + 2*x3 <= 120, name="iron_ore_constraint")
    model.addConstr(2*x1 + 5*x2 + 3.5*x3 <= 70, name="zinc_ore_constraint")

    # Optimize the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"General purpose vessels: {x1.varValue}")
        print(f"Pharmaceutical vessels: {x2.varValue}")
        print(f"Pressure vessels: {x3.varValue}")
        print(f"Maximum profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```