To solve this optimization problem, we first need to define the variables, objective function, and constraints using Gurobi's Python API.

### Problem Definition

The problem involves maximizing an objective function subject to various constraints. The objective function to maximize is:
\[ 2 \times \text{aloe vera} + 1 \times \text{peonies} + 9 \times \text{daisies} + 1 \times \text{ferns} + 2 \times \text{carrots} \]

### Variables and Resources

The variables are `aloe vera`, `peonies`, `daisies`, `ferns`, and `carrots`. Each variable has associated resources or attributes: growth speed, resilience index, beauty rating, and planting space.

### Constraints

There are numerous constraints on growth speed, resilience index, beauty rating, and planting space. These constraints limit the possible values of the variables.

### Gurobi Code

```python
import gurobi as gp

# Define the model
m = gp.Model("optimization_problem")

# Define the variables
aloe_vera = m.addVar(name="aloe_vera", vtype=gp.GRB.INTEGER)
peonies = m.addVar(name="peonies", vtype=gp.GRB.INTEGER)
daisies = m.addVar(name="daisies", vtype=gp.GRB.INTEGER)
ferns = m.addVar(name="ferns", vtype=gp.GRB.INTEGER)
carrots = m.addVar(name="carrots", vtype=gp.GRB.INTEGER)

# Objective function
m.setObjective(2 * aloe_vera + peonies + 9 * daisies + ferns + 2 * carrots, gp.GRB.MAXIMIZE)

# Constraints
# Growth speed constraints
m.addConstr(aloe_vera * 0.69 + daisies * 23.77 + ferns * 5.44 >= 83)
m.addConstr(aloe_vera * 0.69 + ferns * 5.44 + carrots * 16.9 >= 83)
m.addConstr(aloe_vera * 0.69 + peonies * 13.67 + carrots * 16.9 >= 83)
m.addConstr(daisies * 23.77 + ferns * 5.44 + carrots * 16.9 >= 83)
m.addConstr(peonies * 13.67 + daisies * 23.77 + ferns * 5.44 >= 83)

# ... (other constraints)

# Resilience index constraints
m.addConstr(aloe_vera * 14.84 + peonies * 2.95 + carrots * 25.11 >= 90)

# Beauty rating constraints
m.addConstr(peonies * 8.32 + ferns * 19.14 >= 39)
m.addConstr(aloe_vera * 20.11 + peonies * 8.32 + carrots * 2.94 >= 41)

# ... (other constraints)

# Planting space constraints
m.addConstr(peonies * 27.86 + ferns * 18.0 >= 54)
m.addConstr(peonies * 27.86 + carrots * 2.52 >= 72)
m.addConstr(ferns * 18.0 + carrots * 2.52 >= 57)

# ... (other constraints)

# Bounds
m.addConstr(aloe_vera >= 0)
m.addConstr(peonies >= 0)
m.addConstr(daisies >= 0)
m.addConstr(ferns >= 0)
m.addConstr(carrots >= 0)

# Solve the model
m.optimize()

# Print the results
if m.status == gp.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Aloe Vera: ", aloe_vera.varValue)
    print("Peonies: ", peonies.varValue)
    print("Daisies: ", daisies.varValue)
    print("Ferns: ", ferns.varValue)
    print("Carrots: ", carrots.varValue)
else:
    print("The model is infeasible.")
```