## Step 1: Define the symbolic representation of the variables
The variables are 'squash plants' and 'cherry trees', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $2.74x_1^2 + 5.48x_1x_2 + 8.46x_2^2 + 5.27x_1 + 2.9x_2$.

## 3: Define the constraints in symbolic notation
The constraints are:
- $33x_1 + 25x_2 \geq 101$ (total combined beauty rating minimum)
- $x_1^2 + x_2^2 \geq 101$ (total combined beauty rating from squares minimum)
- $11x_1 + 15x_2 \geq 143$ (total planting space minimum)
- $11x_1 + 30x_2 \geq 75$ (total water need minimum)
- $3x_1 - x_2 \geq 0$ (squash plants and cherry trees relationship)
- $33x_1 + 25x_2 \leq 206$ (total combined beauty rating maximum)
- $11^2x_1^2 + 15^2x_2^2 \leq 159$ (total planting space squared maximum, corrected for proper calculation: $121x_1^2 + 225x_2^2 \leq 159$ is incorrect based on the problem, it seems it should relate to $x_1^2 + x_2^2 \leq 159$ for space, but properly it is $11x_1 + 15x_2 \leq \sqrt{159*11^2+15^2}$ or directly using given numbers)
- $11^2x_1^2 + 30^2x_2^2 \leq 162$ (total water need squared maximum, $121x_1^2 + 900x_2^2 \leq 162$)
- $x_1 \in \mathbb{Z}$ and $x_2 \in \mathbb{Z}$ (integer constraints)

Correcting and properly representing:
- The beauty rating constraints: $33x_1 + 25x_2 \geq 101$ and $33x_1 + 25x_2 \leq 206$
- Planting space squared is not directly provided, assuming $x_1^2 + x_2^2 \leq 159$ was mistakenly considered.
- Water need squared: $121x_1^2 + 900x_2^2 \leq 162$

## 4: Properly formulate the problem in Gurobi
We need to model this problem using Gurobi, ensuring all constraints and the objective function are correctly implemented.

## 5: Output the symbolic representation
```json
{
'sym_variables': [('x1', 'squash plants'), ('x2', 'cherry trees')],
'objective_function': '2.74*x1^2 + 5.48*x1*x2 + 8.46*x2^2 + 5.27*x1 + 2.9*x2',
'constraints': [
    '33*x1 + 25*x2 >= 101',
    'x1^2 + x2^2 >= 101',
    '11*x1 + 15*x2 >= 143',
    '11*x1 + 30*x2 >= 75',
    '3*x1 - x2 >= 0',
    '33*x1 + 25*x2 <= 206',
    '121*x1^2 + 225*x2^2 <= 159',
    '121*x1^2 + 900*x2^2 <= 162'
]
}
```

## 6: Implement the problem in Gurobi
```python
import gurobi

def optimize_problem():
    model = gurobi.Model()
    
    # Define variables
    x1 = model.addVar(name="squash_plants", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="cherry_trees", vtype=gurobi.GRB.INTEGER)
    
    # Objective function
    model.setObjective(2.74*x1**2 + 5.48*x1*x2 + 8.46*x2**2 + 5.27*x1 + 2.9*x2, gurobi.GRB.MINIMIZE)
    
    # Constraints
    model.addConstr(33*x1 + 25*x2 >= 101)
    model.addConstr(x1**2 + x2**2 >= 101)
    model.addConstr(11*x1 + 15*x2 >= 143)
    model.addConstr(11*x1 + 30*x2 >= 75)
    model.addConstr(3*x1 - x2 >= 0)
    model.addConstr(33*x1 + 25*x2 <= 206)
    model.addConstr(121*x1**2 + 225*x2**2 <= 159)
    model.addConstr(121*x1**2 + 900*x2**2 <= 162)
    
    # Solve the problem
    model.optimize()
    
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Squash plants: {x1.varValue}, Cherry trees: {x2.varValue}")
    elif model.status == gurobi.GRB.INFEASIBLE:
        print("The problem is infeasible.")
    else:
        print("The model status is: ", model.status)

optimize_problem()
```