## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the acres of potatoes
- $x_2$ represents the acres of squash

## Step 2: Formulate the objective function
The profit from an acre of potatoes is $700 and from an acre of squash is $144. The objective function to maximize profit is:
\[ \text{Maximize:} \quad 700x_1 + 144x_2 \]

## 3: Define the constraints
1. Total land constraint: The farming group has 1000 acres of land.
\[ x_1 + x_2 \leq 1000 \]
2. Tractor time constraint: They have at most 1200 hours of tractor time available. Each acre of potatoes requires 20 hours and each acre of squash requires 23 hours.
\[ 20x_1 + 23x_2 \leq 1200 \]
3. Capital constraint: They have $26400 of capital available. Each acre of potatoes requires $10 and each acre of squash requires $110.
\[ 10x_1 + 110x_2 \leq 26400 \]
4. Non-negativity constraints: The acres of potatoes and squash cannot be negative.
\[ x_1 \geq 0, x_2 \geq 0 \]

## 4: Symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'acres of potatoes'), ('x2', 'acres of squash')],
'objective_function': '700*x1 + 144*x2',
'constraints': [
    'x1 + x2 <= 1000',
    '20*x1 + 23*x2 <= 1200',
    '10*x1 + 110*x2 <= 26400',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 5: Gurobi code to solve the problem
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(lb=0, name="potatoes")
    x2 = model.addVar(lb=0, name="squash")

    # Objective function
    model.setObjective(700*x1 + 144*x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(x1 + x2 <= 1000, name="land_constraint")
    model.addConstr(20*x1 + 23*x2 <= 1200, name="tractor_time_constraint")
    model.addConstr(10*x1 + 110*x2 <= 26400, name="capital_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Acres of potatoes: {x1.varValue}")
        print(f"Acres of squash: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```