## Problem Description and Symbolic Representation

The problem can be broken down into the following components:

- **Variables:**
  - Let \(x_1\) be the acres of beans.
  - Let \(x_2\) be the acres of pumpkins.

- **Objective Function:**
  - The profit per acre of beans is $100, and the profit per acre of pumpkins is $110. The objective is to maximize profit, so the objective function is \(100x_1 + 110x_2\).

- **Constraints:**
  1. Total acres: \(x_1 + x_2 \leq 100\)
  2. Minimum acres of beans: \(x_1 \geq 5\)
  3. Minimum acres of pumpkins: \(x_2 \geq 10\)
  4. Labor constraint (pumpkins to beans ratio): \(x_2 \leq 3x_1\)

## Symbolic Representation

```json
{
  'sym_variables': [('x1', 'acres of beans'), ('x2', 'acres of pumpkins')],
  'objective_function': '100*x1 + 110*x2',
  'constraints': [
    'x1 + x2 <= 100',
    'x1 >= 5',
    'x2 >= 10',
    'x2 <= 3*x1'
  ]
}
```

## Gurobi Code

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(lb=0, name="beans")
    x2 = model.addVar(lb=0, name="pumpkins")

    # Set objective function
    model.setObjective(100 * x1 + 110 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(x1 + x2 <= 100, name="total_acres")
    model.addConstr(x1 >= 5, name="min_beans")
    model.addConstr(x2 >= 10, name="min_pumpkins")
    model.addConstr(x2 <= 3 * x1, name="labor_constraint")

    # Update model
    model.update()

    # Optimize model
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal acres of beans: {x1.varValue}")
        print(f"Optimal acres of pumpkins: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("The model is infeasible")

solve_optimization_problem()
```